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University of Texas – Austin
1.
Buddhavarapu, Prasad Naga Venkata Siva Rama.
Modeling unobserved heterogeneity of spatially correlated count data using finite-mixture random parameters.
Degree: MSin Statistics, Statistics, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/32501 
► The main goal of this research is to propose a specification to model the unobserved heterogeneity in count outcomes. A negative binomial likelihood is utilized…
(more)
▼ The main goal of this research is to propose a specification to model the unobserved heterogeneity in count outcomes. A negative binomial likelihood is utilized for modeling count data. Unobserved heterogeneity is modeled using random model parameters with finite multi-variate normal mixture prior structure. The model simultaneously accounts for potential spatial correlation of crash counts from neighboring units. The model extracts the inherent groups of road segments with crash counts that are equally sensitive to the road attributes on an average; the heterogeneity within these groups is also allowed in the proposed framework. This research employs a computationally efficient Bayesian estimation framework to perform statistical inference of the proposed model. A Markov Chain Monte Carlo (MCMC) sampling strategy is proposed that leverages recent theoretical developments in data-augmentation algorithms, and elegantly sidesteps many of the computational difficulties usually associated with Bayesian inference of count models.
Advisors/Committee Members: Scott, James (Statistician) (advisor), Prozzi, Jorge A (committee member).
Subjects/Keywords: Finite-mixture models; Negative-binomial; Unobserved heterogeneity; Bayesian inference; Data-augmentation
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APA (6th Edition):
Buddhavarapu, P. N. V. S. R. (2015). Modeling unobserved heterogeneity of spatially correlated count data using finite-mixture random parameters. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32501
Chicago Manual of Style (16th Edition):
Buddhavarapu, Prasad Naga Venkata Siva Rama. “Modeling unobserved heterogeneity of spatially correlated count data using finite-mixture random parameters.” 2015. Masters Thesis, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/32501.
MLA Handbook (7th Edition):
Buddhavarapu, Prasad Naga Venkata Siva Rama. “Modeling unobserved heterogeneity of spatially correlated count data using finite-mixture random parameters.” 2015. Web. 28 Feb 2021.
Vancouver:
Buddhavarapu PNVSR. Modeling unobserved heterogeneity of spatially correlated count data using finite-mixture random parameters. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/32501.
Council of Science Editors:
Buddhavarapu PNVSR. Modeling unobserved heterogeneity of spatially correlated count data using finite-mixture random parameters. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32501
University of Texas – Austin
2.
Gillett, Carlos Townes.
A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions.
Degree: MSin Statistics, Statistics, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/32494
► This report compares the convergence behavior of the Metropolis-Hastings and an alternative Markov Chain Monte Carlo sampling algorithm targeting unnormalized, discrete distributions with countably infinite…
(more)
▼ This report compares the convergence behavior of the Metropolis-Hastings and an alternative Markov Chain Monte Carlo sampling algorithm targeting unnormalized, discrete distributions with countably infinite sample spaces. The two methods are compared through a simulation study in which each is used to generate samples from a known distribution. We find that the alternative sampler generates increasingly independent samples as the scale parameter is increased, in contrast to the Metropolis-Hastings. These results suggest that, regardless of the target distribution, our alternative algorithm can generate Markov chains with less autocorrelation than even an optimally scaled Metropolis-Hastings algorithm. We conclude that this alternative algorithm represents a valuable addition to extant Markov Chain Monte Carlo Methods.
Advisors/Committee Members: Walker, Stephen G., 1945- (advisor), Scott, James (committee member).
Subjects/Keywords: Metropolis-Hastings; Bayesian inference; Unnormalized probabilities
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APA (6th Edition):
Gillett, C. T. (2015). A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32494
Chicago Manual of Style (16th Edition):
Gillett, Carlos Townes. “A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions.” 2015. Masters Thesis, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/32494.
MLA Handbook (7th Edition):
Gillett, Carlos Townes. “A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions.” 2015. Web. 28 Feb 2021.
Vancouver:
Gillett CT. A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/32494.
Council of Science Editors:
Gillett CT. A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32494
3.
Madrid Padilla, Oscar Hernan.
Constrained estimation via the fused lasso and some generalizations.
Degree: PhD, Statistics, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/63067
► This dissertation studies structurally constrained statistical estimators. Two entwined main themes are developed: computationally efficient algorithms, and strong statistical guarantees of estimators across a wide…
(more)
▼ This dissertation studies structurally constrained statistical estimators. Two entwined main themes are developed: computationally efficient algorithms, and strong statistical guarantees of estimators across a wide range of frameworks.
In the first chapter we discuss a unified view of optimization problems that enforces constrains, such as smoothness, in statistical inference. This in turn helps to incorporate spatial and/or temporal information about
data.
The second chapter studies the fused lasso, a non-parametric regression estimator commonly used for graph denoising. This has been widely used in applications where the graph structure indicates that neighbor nodes
have similar signal values. I prove for the fused lasso on arbitrary graphs, an upper bound on the mean squared error that depends on the total variation
of the underlying signal on the graph. Moreover, I provide a surrogate estimator that can be found in linear time and attains the same upper–bound.
In the third chapter I present an approach for penalized tensor decomposition (PTD) that estimates smoothly varying latent factors in multiway data. This generalizes existing work on sparse tensor decomposition
and penalized matrix decomposition, in a manner parallel to the generalized lasso for regression and smoothing problems. I present an efficient coordinate-wise optimization algorithm for PTD, and characterize its convergence properties.
The fourth chapter proposes histogram trend filtering, a novel approach for density estimation. This estimator arises from looking at surrogate Poisson model for counts of observations in a partition of the support
of the data.
The fifth chapter develops a class of estimators for deconvolution in mixture models based on a simple two-step bin-and-smooth procedure, applied to histogram counts. The method is both statistically and computationally efficient. By exploiting recent advances in convex optimization,we are able to provide a full deconvolution path that shows the estimate for
the mixing distribution across a range of plausible degrees of smoothness, at far less cost than a full Bayesian analysis.
Finally, the sixth chapter summarizes my contributions and provides possible directions for future work.
Advisors/Committee Members: Scott, James (Statistician) (advisor), Caramanis, Constantine (committee member), Sarkar, Purnamrita (committee member), Zhou, Mingyuan (committee member).
Subjects/Keywords: Fused lasso; Penalized likelihood.
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APA (6th Edition):
Madrid Padilla, O. H. (2017). Constrained estimation via the fused lasso and some generalizations. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63067
Chicago Manual of Style (16th Edition):
Madrid Padilla, Oscar Hernan. “Constrained estimation via the fused lasso and some generalizations.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/63067.
MLA Handbook (7th Edition):
Madrid Padilla, Oscar Hernan. “Constrained estimation via the fused lasso and some generalizations.” 2017. Web. 28 Feb 2021.
Vancouver:
Madrid Padilla OH. Constrained estimation via the fused lasso and some generalizations. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/63067.
Council of Science Editors:
Madrid Padilla OH. Constrained estimation via the fused lasso and some generalizations. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63067
University of Texas – Austin
4.
Garrette, Daniel Hunter.
Inducing grammars from linguistic universals and realistic amounts of supervision.
Degree: PhD, Artificial intelligence, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/44478
► The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance…
(more)
▼ The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance can be achieved with plentiful supervised data. However, such resources are extremely costly to produce, making them an unlikely option for building NLP tools in under-resourced languages or domains. This dissertation is concerned with reducing the annotation required to learn NLP models, with the goal of opening up the range of domains and languages to which NLP technologies may be applied. In this work, we explore the possibility of learning from a degree of supervision that is at or close to the amount that could reasonably be collected from annotators for a particular domain or language that currently has none. We show that just a small amount of annotation input — even that which can be collected in just a few hours — can provide enormous advantages if we have learning algorithms that can appropriately exploit it. This work presents new algorithms, models, and approaches designed to learn grammatical information from weak supervision. In particular, we look at ways of intersecting a variety of different forms of supervision in complementary ways, thus lowering the overall annotation burden. Sources of information include tag dictionaries, morphological analyzers, constituent bracketings, and partial tree annotations, as well as unannotated corpora. For example, we present algorithms that are able to combine faster-to-obtain type-level annotation with unannotated text to remove the need for slower-to-obtain token-level annotation. Much of this dissertation describes work on Combinatory Categorial Grammar (CCG), a grammatical formalism notable for its use of structured, logic-backed categories that describe how each word and constituent fits into the overall syntax of the sentence. This work shows how linguistic universals intrinsic to the CCG formalism itself can be encoded as Bayesian priors to improve learning.
Advisors/Committee Members: Baldridge, Jason (advisor), Mooney, Raymond J. (Raymond Joseph) (advisor), Ravikumar, Pradeep (committee member), Scott, James G (committee member), Smith, Noah A (committee member).
Subjects/Keywords: Computer science; Artificial intelligence; Natural language processing; Machine learning; Bayesian statistics; Grammar induction; Parsing; Computational linguistics
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APA (6th Edition):
Garrette, D. H. (2015). Inducing grammars from linguistic universals and realistic amounts of supervision. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44478
Chicago Manual of Style (16th Edition):
Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/44478.
MLA Handbook (7th Edition):
Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Web. 28 Feb 2021.
Vancouver:
Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/44478.
Council of Science Editors:
Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/44478
University of Texas – Austin
5.
-2304-7384.
Improving surveillance and prediction of emerging and re-emerging infectious diseases.
Degree: PhD, Cell and Molecular Biology, 2020, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/7632
► Infectious diseases are emerging at an unprecedent rate in recent years, such as the flu pandemic initialized from Mexico in 2009, the 2014 Ebola epidemic…
(more)
▼ Infectious diseases are emerging at an unprecedent rate in recent years, such as the flu pandemic initialized from Mexico in 2009, the 2014 Ebola epidemic in West Africa, and the 2016-2017 expansion of Zika across Americas. They rarely happened previously and thus lack resources and data to detect and predict their spread. This highlights the challenges in emerging an re-emerging infectious disease surveillance. In the dissertation, I mainly put efforts in developing methods for early detection of such diseases, and assessing predictive power of various models in early phase of an epidemic. In Chapter 2, I developed a two-layer early detection framework which provides early warning of emerging epidemics based on the idea of anomaly detection. The framework could evaluate and identify data sources to achieve the best performance automatically from available data, such as data from the Internet and public health surveillance systems. I demonstrated the framework using historical influenza data in the US, and found that the optimal combination of predictors includes data sources from Google search query and Wikipedia page view. The optimized system is able to detect the onset of seasonal influenza outbreaks an average of 16.4 weeks in advance, and the second wave of the 2009 flu pandemic 5 weeks ahead. In Chapter 3, I extended the framework in Chapter 2 to identify large dengue outbreaks from small ones. The results show that the framework could personalize optimal combinations of predictors for different locations, and an optimal combination for one location might not perform well for other locations. In Chapter 4, I investigated the contribution of different population structures to total epidemic incidence, peak intensity and timing, and also explored the ability of various models with different population structures in predicting epidemic dynamics. The results suggest that heterogeneous contact pattern and direct contacts dominate the evolution of epidemics, and a homogeneous model is not able to provide reliable prediction for an epidemic. In summary, my dissertation not only provides method frameworks for building early detection systems for emerging and re-emerging infectious diseases, but also gives insight to the effects of various models in predicting epidemics.
Advisors/Committee Members: Meyers, Lauren Ancel (advisor), Wilke, Claus O. (committee member), Bull, James J. (committee member), Hillis, David (committee member), Scott, James (committee member).
Subjects/Keywords: Emerging infectious diseases; Re-emerging infectious diseases; Surveillance; Early detection; Prediction; Mathematical models; Statistical models
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APA (6th Edition):
-2304-7384. (2020). Improving surveillance and prediction of emerging and re-emerging infectious diseases. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/7632
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-2304-7384. “Improving surveillance and prediction of emerging and re-emerging infectious diseases.” 2020. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://dx.doi.org/10.26153/tsw/7632.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-2304-7384. “Improving surveillance and prediction of emerging and re-emerging infectious diseases.” 2020. Web. 28 Feb 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-2304-7384. Improving surveillance and prediction of emerging and re-emerging infectious diseases. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2020. [cited 2021 Feb 28].
Available from: http://dx.doi.org/10.26153/tsw/7632.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-2304-7384. Improving surveillance and prediction of emerging and re-emerging infectious diseases. [Doctoral Dissertation]. University of Texas – Austin; 2020. Available from: http://dx.doi.org/10.26153/tsw/7632
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
6.
-9322-9685.
Discovering latent structures in syntax trees and mixed-type data.
Degree: PhD, Operations Research and Industrial Engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/68368
► Gibbs sampling is a widely applied algorithm to estimate parameters in statistical models. This thesis uses Gibbs sampling to resolve practical problems, especially on natural…
(more)
▼ Gibbs sampling is a widely applied algorithm to estimate parameters in statistical models. This thesis uses Gibbs sampling to resolve practical problems, especially on natural language processing and mixed type data. It includes three independent studies. The first study includes a Bayesian model for learning latent annotations. The technique is capable of parsing sentences in a wide variety of languages, producing results that are on-par with or surpass previous approaches in accuracy, and shows promising potential for parsing low-resource languages. The second study presents a method to automatically complete annotations from partially-annotated sentence data, with the help of Gibbs sampling. The algorithm significantly reduces the time required to annotate sentences for natural language processing, without a significant drop in annotation accuracy. The last study proposes a novel factor model for uncovering latent factors and exploring covariation among multiple outcomes of mixed types, including binary, count, and continuous data. Gibbs sampling is used to estimate model parameters. The algorithm successfully discovers correlation structures of mixed-type
data in both simulated and real-word data.
Advisors/Committee Members: Dimitrov, Nedialko B. (advisor), Baldridge, Jason (committee member), Hasenbein, John (committee member), Khajavirad, Aida (committee member), Scott, James (committee member).
Subjects/Keywords: Gibbs sampling; Natural language processing; Bayesian statistics; Factor analysis; Syntax trees parsing
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APA (6th Edition):
-9322-9685. (2016). Discovering latent structures in syntax trees and mixed-type data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68368
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-9322-9685. “Discovering latent structures in syntax trees and mixed-type data.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/68368.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-9322-9685. “Discovering latent structures in syntax trees and mixed-type data.” 2016. Web. 28 Feb 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-9322-9685. Discovering latent structures in syntax trees and mixed-type data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/68368.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-9322-9685. Discovering latent structures in syntax trees and mixed-type data. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/68368
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
7.
-9309-5664.
Regularization in econometrics and finance.
Degree: PhD, Statistics, 2018, University of Texas – Austin
URL: http://hdl.handle.net/2152/65998
► This dissertation develops regularization methods for use in finance and econometrics problems. The key methodology introduced is utility-based selection (UBS) – a procedure for inducing…
(more)
▼ This dissertation develops regularization methods for use in finance and econometrics problems. The key methodology introduced is utility-based selection (UBS) – a procedure for inducing sparsity in statistical models and practical problems requiring the need for simple and parsimonious decisions.
The introduction section describes statistical model selection in light of the "big data hype" and desire to fit rich and complex models. Key emphasis is placed on the fundamental bias-variance tradeoff in statistics. The remaining portions of the introduction tie these notions into the components and procedure of UBS. This latter half frames model selection as a decision and develops the procedure using decision-theoretic principles.
The second chapter applies UBS to portfolio optimization. A dynamic portfolio construction framework is presented, and the asset returns are modeled using a Bayesian dynamic linear model. The focus here is constructing simple, or sparse, portfolios of passive funds. We consider a set of the most liquid exchange traded funds for our empirical analysis.
The third chapter discusses variable selection in seemingly unrelated regression models (SURs). UBS is applied in this context where an analyst wants to find, among p available predictors, what subset are most relevant for describing variation in q different responses. The selection procedure takes into account uncertainty in both the responses and predictors. It is applied to a popular problem in asset pricing – discovering which factors (predictors) are relevant for pricing the cross section of asset returns (responses). We also discuss future work in monotonic function estimation and how UBS is applied in this context.
The fourth chapter considers regularization in treatment effect estimation using linear regression. It introduces "regularization-induced confounding" (RIC), a pitfall of employing naive regularization techniques for estimating a treatment effect from observational data. A new model parameterization is presented that mitigates RIC. Additionally, we discuss recent work that considers uncertainty characterization when model errors may vary by clusters of data. These developments employ empirical-Bayes and bootstrapping techniques.
Advisors/Committee Members: Carvalho, Carlos Marinho, 1978- (advisor), Hahn, P. Richard (committee member), Scott, James (committee member), Williamson, Sinead (committee member), Titman, Sheridan (committee member).
Subjects/Keywords: Utility-based posterior summarization; Asset pricing; Cross-section of returns
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APA ·
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APA (6th Edition):
-9309-5664. (2018). Regularization in econometrics and finance. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65998
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-9309-5664. “Regularization in econometrics and finance.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/65998.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-9309-5664. “Regularization in econometrics and finance.” 2018. Web. 28 Feb 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-9309-5664. Regularization in econometrics and finance. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/65998.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-9309-5664. Regularization in econometrics and finance. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/65998
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
8.
Zhang, Michael Minyi.
Scalable inference for Bayesian non-parametrics.
Degree: PhD, Statistics, 2018, University of Texas – Austin
URL: http://hdl.handle.net/2152/65734
► Bayesian non-parametric models, despite their theoretical elegance, face a serious computational burden that prevents their use in serious "big data'' scenarios. Furthermore, we cannot expect…
(more)
▼ Bayesian non-parametric models, despite their theoretical elegance, face a serious computational burden that prevents their use in serious "big data'' scenarios. Furthermore, we cannot expect the data in "big data'' to exist solely on one processor, so we must have parallel algorithms that are valid Bayesian inference samplers. However, inherent dependencies in Bayesian non-parametric models make this task very difficult. Instead, we must either construct good approximations or develop clever reformulations of our models so that we perform inference with provably accurate results. This thesis will discuss four methods developed to parallelize inference in the Bayesian and Bayesian non-parametric setting.
Advisors/Committee Members: Williamson, Sinead (advisor), Mueller, Peter (committee member), Scott, James G (committee member), Xing, Eric P (committee member).
Subjects/Keywords: Bayesian non-parametrics; Scalable inference; Machine learning
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APA ·
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APA (6th Edition):
Zhang, M. M. (2018). Scalable inference for Bayesian non-parametrics. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65734
Chicago Manual of Style (16th Edition):
Zhang, Michael Minyi. “Scalable inference for Bayesian non-parametrics.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/65734.
MLA Handbook (7th Edition):
Zhang, Michael Minyi. “Scalable inference for Bayesian non-parametrics.” 2018. Web. 28 Feb 2021.
Vancouver:
Zhang MM. Scalable inference for Bayesian non-parametrics. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/65734.
Council of Science Editors:
Zhang MM. Scalable inference for Bayesian non-parametrics. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/65734
9.
Liu, Zhuping.
Push and pull : targeting and couponing in mobile marketing.
Degree: PhD, Marketing, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/62095
► The prevalence of mobile marketing practices has profoundly changed the way consumers shop. Consumers are increasingly shifting to mobile coupons to enhance their shopping experiences.…
(more)
▼ The prevalence of mobile marketing practices has profoundly changed the way consumers shop. Consumers are increasingly shifting to mobile coupons to enhance their shopping experiences. This shift to mobile has created unique opportunities for marketers to engage and target consumers who both actively pull coupons from the mobile app and passively receive targeted push messages about coupons. The literature in mobile marketing is new and numerous issues have not yet been studied. My dissertation examines two such issues in mobile marketing to advance our understanding of the role of mobile promotions in consumers’ shopping journeys and to explore effective personalization strategies in mobile marketing. My first essay examines the effect of mobile promotions on foot traffic by capturing the dynamic interactions among shopper-initiated and
publisher-initiated activities. Shoppers might receive targeted push messages based on either their individual historical behavior ("behavior-based push") or their current location ("location-based push"). I develop a novel multinomial multivariate point process model, which predicts the dynamic interactions between activities. To overcome computational issues in estimation, I develop a new methodology that allows the model to zoom in to days that include activities and to zoom out of inactive days. My simulation of a 15-day period reveals the following insights. First, a behavior-based push leads to an increase in mobile engagement outside malls of more than 25% and an increase in shopping traffic to online stores of about 24%. Second, a behavior-based push would result in an increase in foot traffic to regional malls of about 5% but to strip malls of only about 0.5%. Third, a behavior-based push leads to an increase in mobile engagement inside malls of more than 19% and in coupon redemptions of about 18%, while a location-based push increases mobile engagement inside malls by about 40% and coupon redemptions by about 25%. Therefore, behavior-based push and location-based push play different roles in influencing shopper-initiated activities. I conclude with implications for publishers, mall owners, and retailers on how to leverage mobile marketing to increase mobile engagement, online traffic, foot traffic, and coupon redemptions. My second essay studies the ranking and personalization of organic and sponsored mobile advertising (or coupons) that mix together when delivered to consumers. The
publisher faces a tradeoff between placing sponsored ads from retailers to receive revenue from advertised retailers and selecting the right organic ads to keep consumers engaged. I propose a consumer mobile search model that can account for the unique factors in our empirical context and answer my research questions. I present model-free evidence for the influence of screen size, whether consumers are in a shopping mall and ad type. I also show how consumer sliding and clicking influence their exit decisions. The proposed counterfactual simulations explore different ways of personalization,…
Advisors/Committee Members: Mahajan, Vijay (advisor), Rao, Raghunath (committee member), Duan, Jun (committee member), Scott, James (committee member), Cunningham, Cotter (committee member).
Subjects/Keywords: Mobile marketing; Targeting; Consumer search
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APA (6th Edition):
Liu, Z. (2017). Push and pull : targeting and couponing in mobile marketing. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62095
Chicago Manual of Style (16th Edition):
Liu, Zhuping. “Push and pull : targeting and couponing in mobile marketing.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/62095.
MLA Handbook (7th Edition):
Liu, Zhuping. “Push and pull : targeting and couponing in mobile marketing.” 2017. Web. 28 Feb 2021.
Vancouver:
Liu Z. Push and pull : targeting and couponing in mobile marketing. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/62095.
Council of Science Editors:
Liu Z. Push and pull : targeting and couponing in mobile marketing. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62095
University of Texas – Austin
10.
Buddhavarapu, Prasad Naga Venkata Siva Rama.
On Bayesian estimation of spatial and dynamic count models using data augmentation techniques : application to road safety management.
Degree: PhD, Civil engineering, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/32525
► Over the past several years, roadway safety management has evolved into data-driven or evidence-based science. The corner stone of a data-driven roadway safety management is…
(more)
▼ Over the past several years, roadway safety management has evolved into data-driven or evidence-based science. The corner stone of a data-driven roadway safety management is the knowledge about useful patterns in the complex crash data. Crash data is often difficult to model with several confounding factors and discrete target variables such as crash counts or crash severity. The major goal of this dissertation was to contribute to the methodological realm of roadway safety management. The research objectives are in two folds: 1) to develop state-of-the-art model specifications for modeling crash data, and 2) to develop a probabilistic model-based site ranking framework. This research addresses methodological issues in crash frequency modeling such as unobserved heterogeneity, spatial correlation, and temporal patterns. Two novel specifications were developed to address these methodological issues: 1) negative binomial spatial with random parameters (NBSRP) modeled as multi-variate normal finite mixture distribution; 2) negative binomial spatial model with dynamic parameters (NBSDP). The NBSRP with finite-mixture specification allows for identifying the underlying sub-groups of road segments, and for skewness and multi-modality in the underlying random parameter distribution. The NBSDP specification employs dynamic linear model (DLM) formulation of the discrete negative binomial count model by exploiting recently developed polya-gamma data-augmentation techniques. NBSDP model facilitates to investigate the evolution of the model parameters over the time and to make safety predictions for a future year. Both NBSRP and NBSDP models simultaneously accounts for potential spatial correlation of crash counts from neighboring road segments. Bayesian methods have been widely used for model building and recently gaining further popularity due to the availability of efficient algorithmic techniques for the parameter estimation. Computationally efficient Bayesian estimation frameworks that leverage recent advances in data augmentation techniques were developed in this research to estimate the proposed count specifications. Bayesian estimation methods also facilitate statistical inference on site ranks, thereby allowing for probabilistic ranking. A computationally efficient site ranking framework was developed incorporating the recent probabilistic ranking techniques towards the end of this dissertation. Overall, this dissertation demonstrates the feasibility of designing Bayesian modeling frameworks for probabilistic roadway safety management, which facilitate online learning. The research ideas presented in this dissertation may be extended to bigger networks to test the feasibility of developing a safety management framework that automatically learns from the latest crash data sources over the time.
Advisors/Committee Members: Prozzi, Jorge Alberto (advisor), Bhat, Chnadra R (committee member), Scott, James G (committee member), Carvalho, Carlos M (committee member), Smit, Andre F (committee member).
Subjects/Keywords: Bayesian statistics; Polya gamma data augmentation; Road safety management; Pavements; Markov chain monte carlo simulation
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APA (6th Edition):
Buddhavarapu, P. N. V. S. R. (2015). On Bayesian estimation of spatial and dynamic count models using data augmentation techniques : application to road safety management. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32525
Chicago Manual of Style (16th Edition):
Buddhavarapu, Prasad Naga Venkata Siva Rama. “On Bayesian estimation of spatial and dynamic count models using data augmentation techniques : application to road safety management.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/32525.
MLA Handbook (7th Edition):
Buddhavarapu, Prasad Naga Venkata Siva Rama. “On Bayesian estimation of spatial and dynamic count models using data augmentation techniques : application to road safety management.” 2015. Web. 28 Feb 2021.
Vancouver:
Buddhavarapu PNVSR. On Bayesian estimation of spatial and dynamic count models using data augmentation techniques : application to road safety management. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/32525.
Council of Science Editors:
Buddhavarapu PNVSR. On Bayesian estimation of spatial and dynamic count models using data augmentation techniques : application to road safety management. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32525
11.
Zhang, Wenjie, active 2013.
The relationships between crime rate and income inequality : evidence from China.
Degree: MSin Statistics, Statistics, 2013, University of Texas – Austin
URL: http://hdl.handle.net/2152/22551
► The main purpose of this study is to determine if a Bayesian approach can better capture and provide reasonable predictions for the complex linkage between…
(more)
▼ The main purpose of this study is to determine if a Bayesian approach can better capture and provide reasonable predictions for the complex linkage between crime and income inequality. In this research, we conduct a model comparison between classical inference and Bayesian inference. The conventional studies on the relationship between crime and income inequality usually employ regression analysis to demonstrate whether these two issues are associated. However, there seems to be lack of use of Bayesian approaches in regard to this matter. Studying the panel data of China from 1993 to 2009, we found that in addition to a linear mixed effects model, a Bayesian hierarchical model with informative prior is also a good model to describe the linkage between crime rate and income inequality. The choice of models really depends on the research needs and data availability.
Advisors/Committee Members: Scott, James (Statistician) (advisor).
Subjects/Keywords: Crime rate; Inequality; Classical inference; Bayesian inference
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APA (6th Edition):
Zhang, Wenjie, a. 2. (2013). The relationships between crime rate and income inequality : evidence from China. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/22551
Chicago Manual of Style (16th Edition):
Zhang, Wenjie, active 2013. “The relationships between crime rate and income inequality : evidence from China.” 2013. Masters Thesis, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/22551.
MLA Handbook (7th Edition):
Zhang, Wenjie, active 2013. “The relationships between crime rate and income inequality : evidence from China.” 2013. Web. 28 Feb 2021.
Vancouver:
Zhang, Wenjie a2. The relationships between crime rate and income inequality : evidence from China. [Internet] [Masters thesis]. University of Texas – Austin; 2013. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/22551.
Council of Science Editors:
Zhang, Wenjie a2. The relationships between crime rate and income inequality : evidence from China. [Masters Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/22551
12.
Lakin, Richard Thomas.
Bayesian hierarchical parametric survival analysis for NBA career longevity.
Degree: MSin Statistics, Statistics, 2012, University of Texas – Austin
URL: http://hdl.handle.net/2152/ETD-UT-2012-05-5631
► In evaluating a prospective NBA player, one might consider past performance in the player’s previous years of competition. In doing so, a general manager may…
(more)
▼ In evaluating a prospective NBA player, one might consider past performance in the player’s previous years of competition. In doing so, a general manager may ask the following questions: Do certain characteristics of a player’s past statistics play a role in how long a player will last in the NBA? In this study, we examine the data from players who entered in the NBA in a five-‐year period (1997-‐1998 through 2001-‐2002 season) by looking at their attributes from their collegiate career to see if they have any effect on their career longevity. We will look at basic statistics take for each of these players, such as field goal percentage, points per game, rebounds per game and assists per game. We aim to use Bayesian survival methods to model these event times, while exploiting the hierarchical nature of the data. We will look at two types of models and perform model diagnostics to determine which of the two we prefer.
Advisors/Committee Members: Scott, James (Statistician) (advisor), Powers, Daniel (committee member).
Subjects/Keywords: Survival; Bayesian; Hierarchical; NBA; Sports
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APA (6th Edition):
Lakin, R. T. (2012). Bayesian hierarchical parametric survival analysis for NBA career longevity. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5631
Chicago Manual of Style (16th Edition):
Lakin, Richard Thomas. “Bayesian hierarchical parametric survival analysis for NBA career longevity.” 2012. Masters Thesis, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/ETD-UT-2012-05-5631.
MLA Handbook (7th Edition):
Lakin, Richard Thomas. “Bayesian hierarchical parametric survival analysis for NBA career longevity.” 2012. Web. 28 Feb 2021.
Vancouver:
Lakin RT. Bayesian hierarchical parametric survival analysis for NBA career longevity. [Internet] [Masters thesis]. University of Texas – Austin; 2012. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5631.
Council of Science Editors:
Lakin RT. Bayesian hierarchical parametric survival analysis for NBA career longevity. [Masters Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5631
13.
-1538-3599.
Spatial pricing empirical evaluation of ride-sourcing trips using the graph-fussed lasso for total variation denoising.
Degree: MSin Statistics, Statistics, 2019, University of Texas – Austin
URL: http://hdl.handle.net/2152/72845
► This study explores the spatial pricing discrimination of ride-sourcing trips using empirical data. We use information from more than 1.1 million rides in Austin, Texas,…
(more)
▼ This study explores the spatial pricing discrimination of ride-sourcing trips using empirical data. We use information from more than 1.1 million rides in
Austin,
Texas, provided by a non-profit transportation network company from a period where the main companies were out of the city. We base the analysis on operational variables such as the waiting or idle time between trips, reaching time, and trip distance. Also, we estimate three different productivity measures to evaluate the impact of the trip destination on the driver continuation payoff. We propose the application of a total variation denoising method that enhances the spatial data interpretation. The selected methodology, known as the graph-fussed lasso (GFL), uses an l₁-norm penalty term that presents a variety of benefits to the denoising process. Specifically, this approach provides local adaptivity; it can adapt to inhomogeneity in the level of smoothness across the graph. Solving the GFL smoothing problem involves convex-optimization methods, we make use of a fast and flexible algorithm that presents scalability and high computational efficiency. The principal contributions of this research effort include a temporal and spatial evaluation of different ride-sourcing productivity measures in the
Austin area, an analysis of ride-sourcing trip pricing and its effect on driver equity, and a description of the principal ride-sourcing travel patterns in the city of
Austin. The main results suggest that drivers with rides ending in the central area present favorable spatial differences in productivity when including the revenue of two consecutive trips. However, the time effect was more contrasting. Weekend rides tend to provide better driver productivity measures.
Advisors/Committee Members: Scott, James (Statistician) (advisor), Machemehl, Randy B. (advisor).
Subjects/Keywords: Ride-sourcing; Ride-sharing; Spatial pricing; Fused lasso; Total variation denoising; Graph smoothing
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APA (6th Edition):
-1538-3599. (2019). Spatial pricing empirical evaluation of ride-sourcing trips using the graph-fussed lasso for total variation denoising. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/72845
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-1538-3599. “Spatial pricing empirical evaluation of ride-sourcing trips using the graph-fussed lasso for total variation denoising.” 2019. Masters Thesis, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/72845.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-1538-3599. “Spatial pricing empirical evaluation of ride-sourcing trips using the graph-fussed lasso for total variation denoising.” 2019. Web. 28 Feb 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-1538-3599. Spatial pricing empirical evaluation of ride-sourcing trips using the graph-fussed lasso for total variation denoising. [Internet] [Masters thesis]. University of Texas – Austin; 2019. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/72845.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-1538-3599. Spatial pricing empirical evaluation of ride-sourcing trips using the graph-fussed lasso for total variation denoising. [Masters Thesis]. University of Texas – Austin; 2019. Available from: http://hdl.handle.net/2152/72845
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
14.
Chancellor, Courtney Marie.
Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009.
Degree: MSin Computational Science, Engineering, and Mathematics, Computational Science, Engineering, and Mathematics, 2012, University of Texas – Austin
URL: http://hdl.handle.net/2152/ETD-UT-2012-05-5551
► The identification of asthma patients most at risk of experiencing an emergency department event is an important step toward lessening public health burdens in the…
(more)
▼ The identification of asthma patients most at risk of experiencing an emergency department event is an important step toward lessening public health burdens in the United States. In this report, the CDC BRFSS Asthma Call Back Survey Data from 2006 to 2009 is explored for potential factors for a predictive model. A metric for classifying the control level of asthma patients is constructed and applied. The data is then used to construct a predictive model for ED events with the rpart algorithm.
Advisors/Committee Members: Meyers, Lauren Ancel (advisor), Scott, James (committee member).
Subjects/Keywords: Asthma; Predictive modeling; rpart; Regression trees
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APA (6th Edition):
Chancellor, C. M. (2012). Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5551
Chicago Manual of Style (16th Edition):
Chancellor, Courtney Marie. “Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009.” 2012. Masters Thesis, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/ETD-UT-2012-05-5551.
MLA Handbook (7th Edition):
Chancellor, Courtney Marie. “Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009.” 2012. Web. 28 Feb 2021.
Vancouver:
Chancellor CM. Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009. [Internet] [Masters thesis]. University of Texas – Austin; 2012. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5551.
Council of Science Editors:
Chancellor CM. Predicting emergency department events due to asthma : results from the BRFSS Asthma Call Back Survey 2006-2009. [Masters Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5551
15.
-8326-9643.
Spatial interpolation with Gaussian processes and spatially varying regression coefficients.
Degree: MSin Statistics, Statistics, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/32508
► Linear regression is undoubtedly one of the most widely used statistical techniques, however because it assumes independent observations it can miss important features of a…
(more)
▼ Linear regression is undoubtedly one of the most widely used statistical techniques, however because it assumes independent observations it can miss important features of a dataset when observations are spatially dependent. This report presents the spatially varying coefficients model, which augments a linear regression with a multivariate Gaussian spatial process to allow regression coefficients to vary over the spatial domain of interest. We develop the mathematics of Gaussian processes and illustrate their use, and demonstrate the spatially varying coefficients model on simulated data. We show that it achieves lower prediction error and a better fit to data than a standard linear regression.
Advisors/Committee Members: Keitt, Timothy H. (advisor), Scott, James G (committee member).
Subjects/Keywords: Spatial statistics; Gaussian process; Spatial interpolation; Spatially varying coefficients
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APA (6th Edition):
-8326-9643. (2015). Spatial interpolation with Gaussian processes and spatially varying regression coefficients. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32508
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-8326-9643. “Spatial interpolation with Gaussian processes and spatially varying regression coefficients.” 2015. Masters Thesis, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/32508.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-8326-9643. “Spatial interpolation with Gaussian processes and spatially varying regression coefficients.” 2015. Web. 28 Feb 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-8326-9643. Spatial interpolation with Gaussian processes and spatially varying regression coefficients. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/32508.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-8326-9643. Spatial interpolation with Gaussian processes and spatially varying regression coefficients. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32508
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
16.
Martin, Stephen Fredrick.
Applying Classification and Regression Trees to manage financial risk.
Degree: MSin Statistics, Statistics, 2012, University of Texas – Austin
URL: http://hdl.handle.net/2152/ETD-UT-2012-05-5428
► This goal of this project is to develop a set of business rules to mitigate risk related to a specific financial decision within the prepaid…
(more)
▼ This goal of this project is to develop a set of business rules to mitigate risk related to a specific financial decision within the prepaid debit card industry. Under certain circumstances issuers of prepaid debit cards may need to decide if funds on hold can be released early for use by card holders prior to the final transaction settlement. After a brief introduction to the prepaid card industry and the financial risk associated with the early release of funds on hold, the paper presents the motivation to apply the CART (Classification and Regression Trees) method. The paper provides a tutorial of the CART algorithms formally developed by Breiman, Friedman, Olshen and Stone in the monograph Classification and Regression Trees (1984), as well as, a detailed explanation of the R programming code to implement the RPART function. (Therneau 2010) Special attention is given to parameter selection and the process of finding an optimal solution that balances complexity against predictive classification accuracy when measured against an independent data set through a cross validation process. Lastly, the paper presents an analysis of the financial risk mitigation based on the resulting business rules.
Advisors/Committee Members: Scott, James (Statistician) (advisor), Carvalho, Carlos M. (committee member), Marti, Nathan C. (committee member).
Subjects/Keywords: CART; Classification and Regression Trees; Breiman; Risk; Prepaid; Debit cards; Rollback; R; RPART; Cross validation
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APA (6th Edition):
Martin, S. F. (2012). Applying Classification and Regression Trees to manage financial risk. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5428
Chicago Manual of Style (16th Edition):
Martin, Stephen Fredrick. “Applying Classification and Regression Trees to manage financial risk.” 2012. Masters Thesis, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/ETD-UT-2012-05-5428.
MLA Handbook (7th Edition):
Martin, Stephen Fredrick. “Applying Classification and Regression Trees to manage financial risk.” 2012. Web. 28 Feb 2021.
Vancouver:
Martin SF. Applying Classification and Regression Trees to manage financial risk. [Internet] [Masters thesis]. University of Texas – Austin; 2012. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5428.
Council of Science Editors:
Martin SF. Applying Classification and Regression Trees to manage financial risk. [Masters Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5428
17.
-5294-4228.
Scalable smoothing algorithms for massive graph-structured data.
Degree: PhD, Computer Science, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/61823
► Probabilistically modeling noisy data is a crucial step in virtually all scientific experiments and engineering pipelines. Recent years have seen the rise of several high-throughput…
(more)
▼ Probabilistically modeling noisy data is a crucial step in virtually all scientific experiments and engineering pipelines. Recent years have seen the rise of several high-throughput techniques in science and a proliferation of cheap sensors in engineering. These dual phenomena have resulted in the generation of massive datasets, each often containing rich, problem-dependent structural dependencies within and between their many observations. Classical ``scalable'' modeling procedures for common tasks such as hypothesis testing and conditional density estimation make the simplifying assumption that the data contains little or no underlying dependency structure. More sophisticated techniques to correct for latent correlations in the data have historically dealt only with small datasets where computational complexity was not a consideration. This creates a clear need for scalable, dependency-aware methods in many areas of computational statistics.
To this end, we develop novel graph-based smoothing algorithms that form the foundations of three new methodologies for large-scale structured statistical inference: False Discovery Rate Smoothing (FDRS), Spatial Density Smoothing (SDS), and Smoothed Dyadic Partitioning (SDP). FDRS improves the power of classical multiple hypothesis testing in the scenario where a dependency graph can be defined over each test site. SDS provides a more sample-efficient marginal density estimator when a dependency graph is defined over multiple distributions such as when observing samples arranged on a spatial grid. Finally, when the dependence is between a set of possible outcome values in a discrete conditional probability distribution, SDP leverages the structure of the space to improve the accuracy of the predictions. We demonstrate the utility of our new procedures via a series of benchmarks and three real-world case studies: fMRI analysis with FDRS, detecting radiological anomalies with SDS, and generative modeling of image data with SDP. All code for FDR smoothing, spatial density smoothing, and smoothed dyadic partitioning is publicly available.
Advisors/Committee Members: Scott, James (Statistician) (advisor), Carvalho, Carlos (committee member), Stone, Peter (committee member), Ghosh, Joydeep (committee member).
Subjects/Keywords: Smoothing; Algorithms; False discovery rate; Spatial smoothing; Total variation; Trend filtering
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APA (6th Edition):
-5294-4228. (2017). Scalable smoothing algorithms for massive graph-structured data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61823
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-5294-4228. “Scalable smoothing algorithms for massive graph-structured data.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/61823.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-5294-4228. “Scalable smoothing algorithms for massive graph-structured data.” 2017. Web. 28 Feb 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-5294-4228. Scalable smoothing algorithms for massive graph-structured data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/61823.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-5294-4228. Scalable smoothing algorithms for massive graph-structured data. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61823
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
18.
Jo, Jason.
Structured low complexity data mining.
Degree: PhD, Mathematics, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/31510
► Due to the rapidly increasing dimensionality of modern datasets many classical approximation algorithms have run into severe computational bottlenecks. This has often been referred to…
(more)
▼ Due to the rapidly increasing dimensionality of modern datasets many classical approximation algorithms have run into severe computational bottlenecks. This has often been referred to as the “curse of dimensionality.” To combat this, low complexity priors have been used as they enable us to design efficient approximation algorithms which are capable of scaling up to these modern datasets. Typically the reduction in computational complexity comes at the expense of accuracy. However, the tradeoffs have been relatively advantageous to the computational scientist. This is typically referred to as the “blessings of dimensionality.” Solving large underdetermined systems of linear equations has benefited greatly from the sparsity low complexity prior. A priori, solving a large underdetermined system of linear equations is severely ill-posed. However, using a relatively generic class of sampling matrices, assuming a sparsity prior can yield a well-posed linear system of equations. In particular, various greedy iterative approximation algorithms have been developed which can recover and accurately approximate the k-most significant atoms in our signal. For many engineering applications, the distribution of the top k atoms is not arbitrary and itself has some further structure. In the first half of the thesis we will be concerned with incorporating some a priori designed weights to allow for structured sparse approximation. We provide performance guarantees and numerically demonstrate how the appropriate use of weights can yield a simultaneous reduction in sample complexity and an improvement in approximation accuracy. In the second half of the thesis we will consider the collaborative filtering problem, specifically the task of matrix completion. The matrix completion problem is likewise severely ill-posed but with a low rank prior, the matrix completion problem with high probability admits a unique and robust solution via a cadre of convex optimization solvers. The drawback here is that the solvers enjoy strong theoretical guarantees only in the uniform sampling regime. Building upon recent work on non-uniform matrix completion, we propose a completely expert-free empirical procedure to design optimization parameters in the form of positive weights which allow for the recovery of arbitrarily sampled low rank matrices. We provide theoretical guarantees for these empirically learned weights and present numerical simulations which again show that encoding prior knowledge in the form of weights for optimization problems can again yield a simultaneous reduction in sample complexity and an improvement in approximation accuracy.
Advisors/Committee Members: Ward, Rachel A. (advisor), Mueller, Peter (committee member), Hadani, Ronny (committee member), Ren, Kui (committee member), Scott, James (committee member).
Subjects/Keywords: Greedy sparse approximation; Weighted matrix completion
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APA (6th Edition):
Jo, J. (2015). Structured low complexity data mining. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31510
Chicago Manual of Style (16th Edition):
Jo, Jason. “Structured low complexity data mining.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed February 28, 2021.
http://hdl.handle.net/2152/31510.
MLA Handbook (7th Edition):
Jo, Jason. “Structured low complexity data mining.” 2015. Web. 28 Feb 2021.
Vancouver:
Jo J. Structured low complexity data mining. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/2152/31510.
Council of Science Editors:
Jo J. Structured low complexity data mining. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31510
. 
Open Access Theses and Dissertations
