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You searched for +publisher:"University of Florida" +contributor:("KHARE,KSHITIJ"). Showing records 1 – 25 of 25 total matches.

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University of Florida

1. Rahman, Syed Hafizur. Cholesky-Based Model Selection and Estimation in Graphical Models.

Degree: PhD, Statistics, 2017, University of Florida

 Covariance estimation for high-dimensional datasets is a fundamental problem in modern day statistics with numerous applications. In these high dimensional datasets, the number of variables… (more)

Subjects/Keywords: cholesky  – convergence  – covariance  – dag  – graphical  – high-dimensional

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APA (6th Edition):

Rahman, S. H. (2017). Cholesky-Based Model Selection and Estimation in Graphical Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0051806

Chicago Manual of Style (16th Edition):

Rahman, Syed Hafizur. “Cholesky-Based Model Selection and Estimation in Graphical Models.” 2017. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0051806.

MLA Handbook (7th Edition):

Rahman, Syed Hafizur. “Cholesky-Based Model Selection and Estimation in Graphical Models.” 2017. Web. 17 Aug 2019.

Vancouver:

Rahman SH. Cholesky-Based Model Selection and Estimation in Graphical Models. [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0051806.

Council of Science Editors:

Rahman SH. Cholesky-Based Model Selection and Estimation in Graphical Models. [Doctoral Dissertation]. University of Florida; 2017. Available from: http://ufdc.ufl.edu/UFE0051806


University of Florida

2. Wang, Jianan. Characterization of Population Genetic Structure and Evolutionary Process in Phytophthora Species.

Degree: PhD, Plant Pathology, 2015, University of Florida

 Plant disease management can no longer be considered a local process. Advances in population genetics have provided novel insights and powerful tools to understand pathogen… (more)

Subjects/Keywords: Coconuts; Genetics; Genotypes; Pathogens; Phytopathology; Phytophthora; Plant pathogens; Population genetics; Population structure; Species; evolution  – genetics  – phytophthora

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APA (6th Edition):

Wang, J. (2015). Characterization of Population Genetic Structure and Evolutionary Process in Phytophthora Species. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049623

Chicago Manual of Style (16th Edition):

Wang, Jianan. “Characterization of Population Genetic Structure and Evolutionary Process in Phytophthora Species.” 2015. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0049623.

MLA Handbook (7th Edition):

Wang, Jianan. “Characterization of Population Genetic Structure and Evolutionary Process in Phytophthora Species.” 2015. Web. 17 Aug 2019.

Vancouver:

Wang J. Characterization of Population Genetic Structure and Evolutionary Process in Phytophthora Species. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0049623.

Council of Science Editors:

Wang J. Characterization of Population Genetic Structure and Evolutionary Process in Phytophthora Species. [Doctoral Dissertation]. University of Florida; 2015. Available from: http://ufdc.ufl.edu/UFE0049623


University of Florida

3. Xu, Xiaofan. Bayesian Variable Selection and Estimation.

Degree: PhD, Statistics, 2014, University of Florida

 The paper considers the classical Bayesian variable selection problem and an important subproblem in which grouping information of predictors is available. We propose the Half… (more)

Subjects/Keywords: Estimators; Estimators for the mean; Linear regression; Modeling; Oracles; Parametric models; Regression analysis; Regression coefficients; Simulations; Statistics; bayesian  – group  – lasso  – selection  – thresholding  – variable

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APA (6th Edition):

Xu, X. (2014). Bayesian Variable Selection and Estimation. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046905

Chicago Manual of Style (16th Edition):

Xu, Xiaofan. “Bayesian Variable Selection and Estimation.” 2014. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0046905.

MLA Handbook (7th Edition):

Xu, Xiaofan. “Bayesian Variable Selection and Estimation.” 2014. Web. 17 Aug 2019.

Vancouver:

Xu X. Bayesian Variable Selection and Estimation. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0046905.

Council of Science Editors:

Xu X. Bayesian Variable Selection and Estimation. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0046905


University of Florida

4. Pal, Subhadip. Development and Analysis of New Markov Chain Monte Carlo (MCMC) Algorithms.

Degree: PhD, Statistics, 2015, University of Florida

 Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing in the last two decades. MCMC methods have enabled the development and use of intricate… (more)

Subjects/Keywords: Autocorrelation; Ergodic theory; Markov chains; Matrices; Modeling; Regression analysis; Sandwiches; Statistical discrepancies; Statistical models; Statistics; envelope  – geometricergodicity  – mcmc

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APA (6th Edition):

Pal, S. (2015). Development and Analysis of New Markov Chain Monte Carlo (MCMC) Algorithms. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049047

Chicago Manual of Style (16th Edition):

Pal, Subhadip. “Development and Analysis of New Markov Chain Monte Carlo (MCMC) Algorithms.” 2015. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0049047.

MLA Handbook (7th Edition):

Pal, Subhadip. “Development and Analysis of New Markov Chain Monte Carlo (MCMC) Algorithms.” 2015. Web. 17 Aug 2019.

Vancouver:

Pal S. Development and Analysis of New Markov Chain Monte Carlo (MCMC) Algorithms. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0049047.

Council of Science Editors:

Pal S. Development and Analysis of New Markov Chain Monte Carlo (MCMC) Algorithms. [Doctoral Dissertation]. University of Florida; 2015. Available from: http://ufdc.ufl.edu/UFE0049047


University of Florida

5. Beyi, Ashenafi Feyisa. Prediction of Dairy Cow Retention-Pay Offs with Factor Screening and K-Nearest Neighbors Algorithms.

Degree: MS, Animal Sciences, 2017, University of Florida

 Retention-Pay Offs (RPO) can be used to rank dairy cows and support culling decisions. Objectives of this study were 1) to identify important herd factors… (more)

Subjects/Keywords: coefficients  – cow  – effects  – elementary  – forests  – herd  – random  – regression  – replacement  – standardized  – variables

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APA (6th Edition):

Beyi, A. F. (2017). Prediction of Dairy Cow Retention-Pay Offs with Factor Screening and K-Nearest Neighbors Algorithms. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0051237

Chicago Manual of Style (16th Edition):

Beyi, Ashenafi Feyisa. “Prediction of Dairy Cow Retention-Pay Offs with Factor Screening and K-Nearest Neighbors Algorithms.” 2017. Masters Thesis, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0051237.

MLA Handbook (7th Edition):

Beyi, Ashenafi Feyisa. “Prediction of Dairy Cow Retention-Pay Offs with Factor Screening and K-Nearest Neighbors Algorithms.” 2017. Web. 17 Aug 2019.

Vancouver:

Beyi AF. Prediction of Dairy Cow Retention-Pay Offs with Factor Screening and K-Nearest Neighbors Algorithms. [Internet] [Masters thesis]. University of Florida; 2017. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0051237.

Council of Science Editors:

Beyi AF. Prediction of Dairy Cow Retention-Pay Offs with Factor Screening and K-Nearest Neighbors Algorithms. [Masters Thesis]. University of Florida; 2017. Available from: http://ufdc.ufl.edu/UFE0051237


University of Florida

6. Huang, Lei. Survival And Reliability Analysis Under Polya Tree Processes Priors.

Degree: PhD, Statistics, 2013, University of Florida

 The thesis consists of three components, which are related to survival and lifetime data analysis. We propose a Bayesian nonparametric approach to testing treatment effects… (more)

Subjects/Keywords: Censored data; Censorship; Data analysis; Null hypothesis; Reliability functions; Sample size; Simulations; Statistical estimation; Statistical models; Statistics; nonparametric  – survival

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APA (6th Edition):

Huang, L. (2013). Survival And Reliability Analysis Under Polya Tree Processes Priors. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0045745

Chicago Manual of Style (16th Edition):

Huang, Lei. “Survival And Reliability Analysis Under Polya Tree Processes Priors.” 2013. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0045745.

MLA Handbook (7th Edition):

Huang, Lei. “Survival And Reliability Analysis Under Polya Tree Processes Priors.” 2013. Web. 17 Aug 2019.

Vancouver:

Huang L. Survival And Reliability Analysis Under Polya Tree Processes Priors. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0045745.

Council of Science Editors:

Huang L. Survival And Reliability Analysis Under Polya Tree Processes Priors. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0045745


University of Florida

7. Martinez Nino, Carlos Alberto. Studies on Statistical Genomics to Improve the Estimation of Allele Frequencies, Breed Composition, Variance Components, and Prediction of Additive Genetic Values.

Degree: PhD, Animal Sciences, 2017, University of Florida

 This dissertation is devoted to the development of novel statistical methods for point estimation and prediction that incorporate genomic information into a series of relevant… (more)

Subjects/Keywords: bayesian-inference  – genetics  – genomic-selection  – graphical-models  – prediction  – statistical-genomics

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APA (6th Edition):

Martinez Nino, C. A. (2017). Studies on Statistical Genomics to Improve the Estimation of Allele Frequencies, Breed Composition, Variance Components, and Prediction of Additive Genetic Values. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0051012

Chicago Manual of Style (16th Edition):

Martinez Nino, Carlos Alberto. “Studies on Statistical Genomics to Improve the Estimation of Allele Frequencies, Breed Composition, Variance Components, and Prediction of Additive Genetic Values.” 2017. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0051012.

MLA Handbook (7th Edition):

Martinez Nino, Carlos Alberto. “Studies on Statistical Genomics to Improve the Estimation of Allele Frequencies, Breed Composition, Variance Components, and Prediction of Additive Genetic Values.” 2017. Web. 17 Aug 2019.

Vancouver:

Martinez Nino CA. Studies on Statistical Genomics to Improve the Estimation of Allele Frequencies, Breed Composition, Variance Components, and Prediction of Additive Genetic Values. [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0051012.

Council of Science Editors:

Martinez Nino CA. Studies on Statistical Genomics to Improve the Estimation of Allele Frequencies, Breed Composition, Variance Components, and Prediction of Additive Genetic Values. [Doctoral Dissertation]. University of Florida; 2017. Available from: http://ufdc.ufl.edu/UFE0051012


University of Florida

8. Aydin, Burak. Statistical Power in Cluster Randomized Trials an Evaluation of Observed and Latent Mean Covariate.

Degree: PhD, Research and Evaluation Methodology - Human Development and Organizational Studies in Education, 2014, University of Florida

 Cluster randomized trials typically represent a substantial investment of time and money. As the use of cluster randomized trials has become more common, it has… (more)

Subjects/Keywords: False positive errors; Mathematical variables; Missing data; Modeling; Multilevel models; Sample size; Simulations; Standard error; Statistical discrepancies; Statistical models; covariates  – crt  – power

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APA (6th Edition):

Aydin, B. (2014). Statistical Power in Cluster Randomized Trials an Evaluation of Observed and Latent Mean Covariate. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046944

Chicago Manual of Style (16th Edition):

Aydin, Burak. “Statistical Power in Cluster Randomized Trials an Evaluation of Observed and Latent Mean Covariate.” 2014. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0046944.

MLA Handbook (7th Edition):

Aydin, Burak. “Statistical Power in Cluster Randomized Trials an Evaluation of Observed and Latent Mean Covariate.” 2014. Web. 17 Aug 2019.

Vancouver:

Aydin B. Statistical Power in Cluster Randomized Trials an Evaluation of Observed and Latent Mean Covariate. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0046944.

Council of Science Editors:

Aydin B. Statistical Power in Cluster Randomized Trials an Evaluation of Observed and Latent Mean Covariate. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0046944


University of Florida

9. Liao, Yuan. On the Strong Law of Large Numbers for Weighted Sums of Random Elements in Banach Spaces.

Degree: PhD, Statistics, 2012, University of Florida

 Let {Vn, n = 1} be a sequence of random elements in a real separable Banachspace and suppose that {Vn, n = 1} is stochastically… (more)

Subjects/Keywords: Banach space; Expected values; Law of large numbers; Lines in space; Mathematical theorems; Mathematics; Probability theory; Random variables; Real lines; Separable spaces; probability  – slln

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APA (6th Edition):

Liao, Y. (2012). On the Strong Law of Large Numbers for Weighted Sums of Random Elements in Banach Spaces. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043915

Chicago Manual of Style (16th Edition):

Liao, Yuan. “On the Strong Law of Large Numbers for Weighted Sums of Random Elements in Banach Spaces.” 2012. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0043915.

MLA Handbook (7th Edition):

Liao, Yuan. “On the Strong Law of Large Numbers for Weighted Sums of Random Elements in Banach Spaces.” 2012. Web. 17 Aug 2019.

Vancouver:

Liao Y. On the Strong Law of Large Numbers for Weighted Sums of Random Elements in Banach Spaces. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0043915.

Council of Science Editors:

Liao Y. On the Strong Law of Large Numbers for Weighted Sums of Random Elements in Banach Spaces. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0043915


University of Florida

10. Roman, Jorge C. Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models.

Degree: PhD, Statistics, 2012, University of Florida

 We consider two widely applicable Bayesian versions of the general linear mixed model (GLMM). These Bayesian GLMMs are created by adopting a proper and an… (more)

Subjects/Keywords: Consistent estimators; Density; Ergodic theory; Estimation methods; Estimators; Markov chains; Simulations; Statistical discrepancies; Statistics; Sufficient conditions; chains  – ergodicity  – geometric  – gibbs  – markov  – sampler

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APA (6th Edition):

Roman, J. C. (2012). Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043966

Chicago Manual of Style (16th Edition):

Roman, Jorge C. “Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models.” 2012. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0043966.

MLA Handbook (7th Edition):

Roman, Jorge C. “Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models.” 2012. Web. 17 Aug 2019.

Vancouver:

Roman JC. Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0043966.

Council of Science Editors:

Roman JC. Convergence Analysis of Block Gibbs Samplers for Bayesian General Linear Mixed Models. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0043966


University of Florida

11. Savenkov, Oleksandr. Novel Methods for Time Series Data in Clinical Studies.

Degree: PhD, Statistics, 2012, University of Florida

 Single subject or n-of-1 research designs have been widely used to evaluate treatment interventions. Many statistical procedures, such as: split-middle trend lines, regression trend line,… (more)

Subjects/Keywords: Autocorrelation; Autoregressive moving average; Data lines; False positive errors; Modeling; Permutation tests; Statistical models; Statistics; Time series; Time series models; design  – series  – single  – subject  – time

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APA (6th Edition):

Savenkov, O. (2012). Novel Methods for Time Series Data in Clinical Studies. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044593

Chicago Manual of Style (16th Edition):

Savenkov, Oleksandr. “Novel Methods for Time Series Data in Clinical Studies.” 2012. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0044593.

MLA Handbook (7th Edition):

Savenkov, Oleksandr. “Novel Methods for Time Series Data in Clinical Studies.” 2012. Web. 17 Aug 2019.

Vancouver:

Savenkov O. Novel Methods for Time Series Data in Clinical Studies. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0044593.

Council of Science Editors:

Savenkov O. Novel Methods for Time Series Data in Clinical Studies. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044593


University of Florida

12. Chen, Yang. Scalable Learning and Inference in Large Knowledge Bases.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2016, University of Florida

 Recent years have seen elevating efforts in the construction of web-scale knowledge bases (e.g., DBPedia, DeepDive, Freebase, Google Knowledge Graph, NELL, OpenIE, ProBase, YAGO). These… (more)

Subjects/Keywords: Algorithms; Databases; Datasets; Inference; Knowledge bases; Learning; Mining; Rules of inference; Semantic networks; Spreading activation models; databases  – datamining  – knowledgebases  – logic  – probability

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APA (6th Edition):

Chen, Y. (2016). Scalable Learning and Inference in Large Knowledge Bases. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0050577

Chicago Manual of Style (16th Edition):

Chen, Yang. “Scalable Learning and Inference in Large Knowledge Bases.” 2016. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0050577.

MLA Handbook (7th Edition):

Chen, Yang. “Scalable Learning and Inference in Large Knowledge Bases.” 2016. Web. 17 Aug 2019.

Vancouver:

Chen Y. Scalable Learning and Inference in Large Knowledge Bases. [Internet] [Doctoral dissertation]. University of Florida; 2016. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0050577.

Council of Science Editors:

Chen Y. Scalable Learning and Inference in Large Knowledge Bases. [Doctoral Dissertation]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0050577


University of Florida

13. Guo, Meixi. Empirical Bayes and Likelihood Based Methods for Measurement Error Models.

Degree: PhD, Statistics, 2012, University of Florida

 Measurement error is a problem that is commonly present in data collected from human respondents. Its early appearance dates back to nearly a century ago… (more)

Subjects/Keywords: Consistent estimators; Estimators; Estimators for the mean; Linear regression; Maximum likelihood estimations; Regression analysis; Simulations; Statism; Statistical estimation; Statistical models; empirical-bayes  – james-stein-estimation  – measurement-error  – profile-likelihood  – small-area-estimation

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APA (6th Edition):

Guo, M. (2012). Empirical Bayes and Likelihood Based Methods for Measurement Error Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044479

Chicago Manual of Style (16th Edition):

Guo, Meixi. “Empirical Bayes and Likelihood Based Methods for Measurement Error Models.” 2012. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0044479.

MLA Handbook (7th Edition):

Guo, Meixi. “Empirical Bayes and Likelihood Based Methods for Measurement Error Models.” 2012. Web. 17 Aug 2019.

Vancouver:

Guo M. Empirical Bayes and Likelihood Based Methods for Measurement Error Models. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0044479.

Council of Science Editors:

Guo M. Empirical Bayes and Likelihood Based Methods for Measurement Error Models. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044479


University of Florida

14. Ali, Mohsen. Deconstructive Learning.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2014, University of Florida

 This dissertation introduces the novel notion of deconstructive learning and it proposes a practical computational framework for deconstructing a broad class of binary classifiers commonly… (more)

Subjects/Keywords: Algorithms; Computer vision; Deconstruction; Image classification; Images of transformations; Learning; Machine learning; Mathematical vectors; Normal vectors; Polynomials; classifiers  – computer-vision  – deconstruction  – machine-learning

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APA (6th Edition):

Ali, M. (2014). Deconstructive Learning. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046149

Chicago Manual of Style (16th Edition):

Ali, Mohsen. “Deconstructive Learning.” 2014. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0046149.

MLA Handbook (7th Edition):

Ali, Mohsen. “Deconstructive Learning.” 2014. Web. 17 Aug 2019.

Vancouver:

Ali M. Deconstructive Learning. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0046149.

Council of Science Editors:

Ali M. Deconstructive Learning. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0046149


University of Florida

15. Gaskins, Jeremy T. Bayesian Methods for Modeling Dependence Structures in Longitudinal Data.

Degree: PhD, Statistics, 2013, University of Florida

 In the modeling of longitudinal data from several groups, appropriate handling of the dependence structure is of central importance.  In this dissertation we consider two… (more)

Subjects/Keywords: Correlations; Covariance; Longitudinal data; Matrices; Parametric models; Sample size; Simulations; Statistical discrepancies; Statistical models; Statistics; bayesian  – correlation  – covariance  – longitudinal  – sparsity

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APA (6th Edition):

Gaskins, J. T. (2013). Bayesian Methods for Modeling Dependence Structures in Longitudinal Data. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0045709

Chicago Manual of Style (16th Edition):

Gaskins, Jeremy T. “Bayesian Methods for Modeling Dependence Structures in Longitudinal Data.” 2013. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0045709.

MLA Handbook (7th Edition):

Gaskins, Jeremy T. “Bayesian Methods for Modeling Dependence Structures in Longitudinal Data.” 2013. Web. 17 Aug 2019.

Vancouver:

Gaskins JT. Bayesian Methods for Modeling Dependence Structures in Longitudinal Data. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0045709.

Council of Science Editors:

Gaskins JT. Bayesian Methods for Modeling Dependence Structures in Longitudinal Data. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0045709

16. Wang, Yi. Scheduling Algorithms and Optimization for Wireless Network and Cloud Computing.

Degree: PhD, Computer Science - Computer and Information Science and Engineering, 2016, University of Florida

 Scheduling for shared resources is of great importance for both the resource provider and the clients. An efficient scheduling algorithm can help increase resource utilization,… (more)

Subjects/Keywords: Algorithms; Customers; Heuristics; Ising model; Markov chains; Scalability; Scheduling; Simulations; Transmitters; Vertices; algorithms  – cloud  – network  – optimization  – scheduling  – wireless

…Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial… 

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APA (6th Edition):

Wang, Y. (2016). Scheduling Algorithms and Optimization for Wireless Network and Cloud Computing. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049852

Chicago Manual of Style (16th Edition):

Wang, Yi. “Scheduling Algorithms and Optimization for Wireless Network and Cloud Computing.” 2016. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0049852.

MLA Handbook (7th Edition):

Wang, Yi. “Scheduling Algorithms and Optimization for Wireless Network and Cloud Computing.” 2016. Web. 17 Aug 2019.

Vancouver:

Wang Y. Scheduling Algorithms and Optimization for Wireless Network and Cloud Computing. [Internet] [Doctoral dissertation]. University of Florida; 2016. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0049852.

Council of Science Editors:

Wang Y. Scheduling Algorithms and Optimization for Wireless Network and Cloud Computing. [Doctoral Dissertation]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0049852


University of Florida

17. Ha, Trung Thanh. Convergence Analysis of Birth-Death Markov Chains and Gibbs Samplers.

Degree: PhD, Statistics, 2016, University of Florida

Subjects/Keywords: birth; chains; death; ergodicity; geometric; gibbs; linear; markov; mixed; models; random; samplers; walk

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APA (6th Edition):

Ha, T. T. (2016). Convergence Analysis of Birth-Death Markov Chains and Gibbs Samplers. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0050242

Chicago Manual of Style (16th Edition):

Ha, Trung Thanh. “Convergence Analysis of Birth-Death Markov Chains and Gibbs Samplers.” 2016. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0050242.

MLA Handbook (7th Edition):

Ha, Trung Thanh. “Convergence Analysis of Birth-Death Markov Chains and Gibbs Samplers.” 2016. Web. 17 Aug 2019.

Vancouver:

Ha TT. Convergence Analysis of Birth-Death Markov Chains and Gibbs Samplers. [Internet] [Doctoral dissertation]. University of Florida; 2016. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0050242.

Council of Science Editors:

Ha TT. Convergence Analysis of Birth-Death Markov Chains and Gibbs Samplers. [Doctoral Dissertation]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0050242


University of Florida

18. Skripnikov, Andrey. Topics in Joint Estimation of Vector Autoregressive Models.

Degree: PhD, Statistics, 2017, University of Florida

In this work we develop various frameworks for performing joint regularized estimation of Advisors/Committee Members: MICHAILIDIS,GEORGE (committee chair), KHARE,KSHITIJ (committee member), PARDALOS,PANAGOTE M (committee member).

Subjects/Keywords: autoregression  – econometrics  – inference  – longitudinal  – multivariate  – neuroimaging  – regularization  – sparsity

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APA (6th Edition):

Skripnikov, A. (2017). Topics in Joint Estimation of Vector Autoregressive Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0051248

Chicago Manual of Style (16th Edition):

Skripnikov, Andrey. “Topics in Joint Estimation of Vector Autoregressive Models.” 2017. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0051248.

MLA Handbook (7th Edition):

Skripnikov, Andrey. “Topics in Joint Estimation of Vector Autoregressive Models.” 2017. Web. 17 Aug 2019.

Vancouver:

Skripnikov A. Topics in Joint Estimation of Vector Autoregressive Models. [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0051248.

Council of Science Editors:

Skripnikov A. Topics in Joint Estimation of Vector Autoregressive Models. [Doctoral Dissertation]. University of Florida; 2017. Available from: http://ufdc.ufl.edu/UFE0051248


University of Florida

19. Zhang, Liyuan. Trace Class Markov Chains for Bayesian Shrinkage Models.

Degree: PhD, Statistics, 2017, University of Florida

High-dimensional data, where the number of variables exceeds or is comparable to Advisors/Committee Members: KHARE,KSHITIJ (committee chair), GHOSH,MALAY (committee member), YANG,YANG (committee member).

Subjects/Keywords: bayesian  – markov  – shrinakge  – trace-class

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APA (6th Edition):

Zhang, L. (2017). Trace Class Markov Chains for Bayesian Shrinkage Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0051351

Chicago Manual of Style (16th Edition):

Zhang, Liyuan. “Trace Class Markov Chains for Bayesian Shrinkage Models.” 2017. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0051351.

MLA Handbook (7th Edition):

Zhang, Liyuan. “Trace Class Markov Chains for Bayesian Shrinkage Models.” 2017. Web. 17 Aug 2019.

Vancouver:

Zhang L. Trace Class Markov Chains for Bayesian Shrinkage Models. [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0051351.

Council of Science Editors:

Zhang L. Trace Class Markov Chains for Bayesian Shrinkage Models. [Doctoral Dissertation]. University of Florida; 2017. Available from: http://ufdc.ufl.edu/UFE0051351


University of Florida

20. Saha, Abhishek. Bayesian Inference in Gaussian Graphical Models when the Underlying Graph Is Non-Decomposable.

Degree: PhD, Statistics, 2016, University of Florida

Subjects/Keywords: bayesian; gaussianmodel; gibbs; graphicalmodel; graphs

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APA (6th Edition):

Saha, A. (2016). Bayesian Inference in Gaussian Graphical Models when the Underlying Graph Is Non-Decomposable. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0050408

Chicago Manual of Style (16th Edition):

Saha, Abhishek. “Bayesian Inference in Gaussian Graphical Models when the Underlying Graph Is Non-Decomposable.” 2016. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0050408.

MLA Handbook (7th Edition):

Saha, Abhishek. “Bayesian Inference in Gaussian Graphical Models when the Underlying Graph Is Non-Decomposable.” 2016. Web. 17 Aug 2019.

Vancouver:

Saha A. Bayesian Inference in Gaussian Graphical Models when the Underlying Graph Is Non-Decomposable. [Internet] [Doctoral dissertation]. University of Florida; 2016. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0050408.

Council of Science Editors:

Saha A. Bayesian Inference in Gaussian Graphical Models when the Underlying Graph Is Non-Decomposable. [Doctoral Dissertation]. University of Florida; 2016. Available from: http://ufdc.ufl.edu/UFE0050408


University of Florida

21. Mukherjee,Nabanita. Asymptotic Variance Evaluations in Discrete Markov Chains.

Degree: PhD, Statistics, 2011, University of Florida

Markov chain Monte Carlo (MCMC) methods have become widely used in various Advisors/Committee Members: Casella, George (committee chair), Ghosh, Malay (committee member), Khare, Kshitij (committee member), Triplett, Eric W (committee member).

Subjects/Keywords: Correlations; Eigenvalues; Markov chains; Matrices; P values; Random variables; Random walk; Statistical discrepancies; Statistics; Type 1 diabetes mellitus; asymptotic  – binomial  – carlo  – case  – chain  – contingency  – control  – correlated  – countable  – gibbs  – hastings  – markov  – metropolis  – monte  – peskun  – sampler  – space  – state  – table  – variance

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APA (6th Edition):

Mukherjee,Nabanita. (2011). Asymptotic Variance Evaluations in Discrete Markov Chains. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043149

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

Mukherjee,Nabanita. “Asymptotic Variance Evaluations in Discrete Markov Chains.” 2011. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0043149.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

Mukherjee,Nabanita. “Asymptotic Variance Evaluations in Discrete Markov Chains.” 2011. Web. 17 Aug 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Mukherjee,Nabanita. Asymptotic Variance Evaluations in Discrete Markov Chains. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0043149.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Mukherjee,Nabanita. Asymptotic Variance Evaluations in Discrete Markov Chains. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043149

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Florida

22. Gopal,Vikneswaran. Techniques of parallelization in Markov chain Monte Carlo methods.

Degree: PhD, Statistics, 2011, University of Florida

Parallel computing is at the forefront of statistical research today. The main Advisors/Committee Members: Casella, George (committee chair), Hobert, James P (committee member), Khare, Kshitij (committee member), Davis, John M (committee member).

Subjects/Keywords: Algorithms; Approximation; Ergodic theory; Markov chains; Pipelines; Point estimators; Random variables; Simulations; Statistics; Tours; amdahl  – clt  – mcmc  – parallel  – regeneration  – renewal  – topic

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APA (6th Edition):

Gopal,Vikneswaran. (2011). Techniques of parallelization in Markov chain Monte Carlo methods. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043220

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

Gopal,Vikneswaran. “Techniques of parallelization in Markov chain Monte Carlo methods.” 2011. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0043220.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

Gopal,Vikneswaran. “Techniques of parallelization in Markov chain Monte Carlo methods.” 2011. Web. 17 Aug 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Gopal,Vikneswaran. Techniques of parallelization in Markov chain Monte Carlo methods. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0043220.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Gopal,Vikneswaran. Techniques of parallelization in Markov chain Monte Carlo methods. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043220

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Florida

23. Gurel, Sungur. Dealing with Selection Bias in Multilevel Observational Studies: An Evaluation of Propensity Score and Direct Estimation Procedures.

Degree: PhD, Research and Evaluation Methodology - Human Development and Organizational Studies in Education, 2015, University of Florida

Subjects/Keywords: bias; data; direct; estimation; mining; multilevel; observational; propensity; score; selection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gurel, S. (2015). Dealing with Selection Bias in Multilevel Observational Studies: An Evaluation of Propensity Score and Direct Estimation Procedures. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049506

Chicago Manual of Style (16th Edition):

Gurel, Sungur. “Dealing with Selection Bias in Multilevel Observational Studies: An Evaluation of Propensity Score and Direct Estimation Procedures.” 2015. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0049506.

MLA Handbook (7th Edition):

Gurel, Sungur. “Dealing with Selection Bias in Multilevel Observational Studies: An Evaluation of Propensity Score and Direct Estimation Procedures.” 2015. Web. 17 Aug 2019.

Vancouver:

Gurel S. Dealing with Selection Bias in Multilevel Observational Studies: An Evaluation of Propensity Score and Direct Estimation Procedures. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0049506.

Council of Science Editors:

Gurel S. Dealing with Selection Bias in Multilevel Observational Studies: An Evaluation of Propensity Score and Direct Estimation Procedures. [Doctoral Dissertation]. University of Florida; 2015. Available from: http://ufdc.ufl.edu/UFE0049506


University of Florida

24. Kampa, Kittipat - Mr. Structured Graphical Models for Unsupervised Image Segmentation.

Degree: PhD, Electrical and Computer Engineering, 2011, University of Florida

In the dissertation, we seek the following goals: (1) to come up with a probabilistic Advisors/Committee Members: Principe, Jose C (committee chair), Rangarajan, Anand (committee member), Harris, John G (committee member), Khare, Kshitij (committee member).

Subjects/Keywords: Algorithms; Bayesian networks; Computer vision; Datasets; Inference; Multisensor fusion; Parametric models; Pixels; Probabilistic modeling; Sensors; bayesian  – cbir  – expectation-maximization  – graphical  – image  – models  – networks  – probabilistic  – segmentation  – superpixel  – tree-structured  – unsupervised

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APA (6th Edition):

Kampa, K. -. M. (2011). Structured Graphical Models for Unsupervised Image Segmentation. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0043604

Chicago Manual of Style (16th Edition):

Kampa, Kittipat - Mr. “Structured Graphical Models for Unsupervised Image Segmentation.” 2011. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0043604.

MLA Handbook (7th Edition):

Kampa, Kittipat - Mr. “Structured Graphical Models for Unsupervised Image Segmentation.” 2011. Web. 17 Aug 2019.

Vancouver:

Kampa K-M. Structured Graphical Models for Unsupervised Image Segmentation. [Internet] [Doctoral dissertation]. University of Florida; 2011. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0043604.

Council of Science Editors:

Kampa K-M. Structured Graphical Models for Unsupervised Image Segmentation. [Doctoral Dissertation]. University of Florida; 2011. Available from: http://ufdc.ufl.edu/UFE0043604


University of Florida

25. Nowrouzian, Roosbeh. Spatial Models for Analyzing the Effects of Land Use Patterns on Automobile Ownership and Usage.

Degree: PhD, Civil Engineering - Civil and Coastal Engineering, 2014, University of Florida

Estimating location-sensitive or spatial models is gaining a lot of interest in Advisors/Committee Members: SRINIVASAN,SIVARAMAKRISHNAN (committee chair), WASHBURN,SCOTT STUART (committee member), YIN,YAFENG (committee member), KHARE,KSHITIJ (committee member), ZWICK,PAUL D (committee member).

Subjects/Keywords: Bandwidth; Land use; Predetermined motion time systems; Regression analysis; Socioeconomics; Spatial models; Statistical models; Transportation; Travel; Urban design; car-ownership  – geographically-weighted-regression  – mixed-geographically-weighted-poisson-regression  – person-mile-traveled  – quasi-geographically-weighted-poisson-regression  – vehicle-distance-traveled  – vehicle-time-traveled

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APA (6th Edition):

Nowrouzian, R. (2014). Spatial Models for Analyzing the Effects of Land Use Patterns on Automobile Ownership and Usage. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0046980

Chicago Manual of Style (16th Edition):

Nowrouzian, Roosbeh. “Spatial Models for Analyzing the Effects of Land Use Patterns on Automobile Ownership and Usage.” 2014. Doctoral Dissertation, University of Florida. Accessed August 17, 2019. http://ufdc.ufl.edu/UFE0046980.

MLA Handbook (7th Edition):

Nowrouzian, Roosbeh. “Spatial Models for Analyzing the Effects of Land Use Patterns on Automobile Ownership and Usage.” 2014. Web. 17 Aug 2019.

Vancouver:

Nowrouzian R. Spatial Models for Analyzing the Effects of Land Use Patterns on Automobile Ownership and Usage. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2019 Aug 17]. Available from: http://ufdc.ufl.edu/UFE0046980.

Council of Science Editors:

Nowrouzian R. Spatial Models for Analyzing the Effects of Land Use Patterns on Automobile Ownership and Usage. [Doctoral Dissertation]. University of Florida; 2014. Available from: http://ufdc.ufl.edu/UFE0046980

.