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You searched for `+publisher:"Utah State University" +contributor:("Daniel C. Coster")`

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Utah State University

1. Li, Yuanzhi. Bayesian Models for Repeated Measures Data Using Markov Chain Monte Carlo Methods.

Degree: PhD, Mathematics and Statistics, 2016, Utah State University

URL: https://digitalcommons.usu.edu/etd/6997

Bayesian models for repeated measures data are fitted to three different data an analysis projects. Markov Chain Monte Carlo (MCMC) methodology is applied to each case with Gibbs sampling and / or an adaptive Metropolis-Hastings (MH ) algorithm used to simulate the posterior distribution of parameters. We implement a Bayesian model with different variance-covariance structures to an audit fee data set. Block structures and linear models for variances are used to examine the linear trend and different behaviors before and after regulatory change during year 2004-2005. We proposed a Bayesian hierarchical model with latent teacher effects, to determine whether teacher professional development (PD) utilizing cyber-enabled resources lead to meaningful student learning outcomes measured by 8th grade student end-of-year scores (CRT scores) for students with teachers who underwent PD. Bayesian variable selection methods are applied to select teacher learning instrument variables to predict teacher effects. We fit a Bayesian two-part model with the first-part a multivariate probit model and the second-p art a log-normal regression to a repeated measures health care data set to analyze the relationship between Body Mass Index (BMI) and health care expenditures and the correlation between the probability of expenditures and dollar amount spent given expenditures. Models were fitted to a training set and predictions were made on both the training set and the test set.
*Advisors/Committee Members: Daniel C. Coster, ;.*

Subjects/Keywords: Bayesian; Models; Markov Chain Monte Carlo; Mathematics; Statistics and Probability

Record Details Similar Records

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

APA (6^{th} Edition):

Li, Y. (2016). Bayesian Models for Repeated Measures Data Using Markov Chain Monte Carlo Methods. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/6997

Chicago Manual of Style (16^{th} Edition):

Li, Yuanzhi. “Bayesian Models for Repeated Measures Data Using Markov Chain Monte Carlo Methods.” 2016. Doctoral Dissertation, Utah State University. Accessed January 22, 2020. https://digitalcommons.usu.edu/etd/6997.

MLA Handbook (7^{th} Edition):

Li, Yuanzhi. “Bayesian Models for Repeated Measures Data Using Markov Chain Monte Carlo Methods.” 2016. Web. 22 Jan 2020.

Vancouver:

Li Y. Bayesian Models for Repeated Measures Data Using Markov Chain Monte Carlo Methods. [Internet] [Doctoral dissertation]. Utah State University; 2016. [cited 2020 Jan 22]. Available from: https://digitalcommons.usu.edu/etd/6997.

Council of Science Editors:

Li Y. Bayesian Models for Repeated Measures Data Using Markov Chain Monte Carlo Methods. [Doctoral Dissertation]. Utah State University; 2016. Available from: https://digitalcommons.usu.edu/etd/6997

Utah State University

2. Li, Chang. Statistical Algorithms for Optimal Experimental Design with Correlated Observations.

Degree: PhD, Mathematics and Statistics, 2013, Utah State University

URL: https://digitalcommons.usu.edu/etd/1507

This research is in three parts with different although related objectives. The first part developed an efficient, modified simulated annealing algorithm to solve the D-optimal (determinant maximization) design problem for 2-way polynomial regression with correlated observations. Much of the previous work in D-optimal design for regression models with correlated errors focused on polynomial models with a single predictor variable, in large part because of the intractability of an analytic solution. In this research, we present an improved simulated annealing algorithm, providing practical approaches to specifications of the annealing cooling parameters, thresholds and search neighborhoods for the perturbation scheme, which finds approximate D-optimal designs for 2-way polynomial regression for a variety of specific correlation structures with a given correlation coefficient. Results in each correlated-errors case are compared with the best design selected from the class of designs that are known to be D-optimal in the uncorrelated case: annealing results had generally higher D-efficiency than the best comparison design, especially when the correlation parameter was well away from 0. The second research objective, using Balanced Incomplete Block Designs (BIBDs), wasto construct weakly universal optimal block designs for the nearest neighbor correlation structure and multiple block sizes, for the hub correlation structure with any block size, and for circulant correlation with odd block size. We also constructed approximately weakly universal optimal block designs for the block-structured correlation. Lastly, we developed an improved Particle Swarm Optimization(PSO) algorithm with time varying parameters, and solved D-optimal design for linear regression with it. Then based on that improved algorithm, we combined the non-linear regression problem and decision making, and developed a nested PSO algorithm that finds (nearly) optimal experimental designs with each of the pessimistic criterion, index of optimism criterion, and regret criterion for the Michaelis-Menten model and logistic regression model.
*Advisors/Committee Members: Daniel C. Coster, ;.*

Subjects/Keywords: D-Optimal Design Problem; 2-Way Polynomial Regression; BIBDs; PSO; Statistics and Probability

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Li, C. (2013). Statistical Algorithms for Optimal Experimental Design with Correlated Observations. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/1507

Chicago Manual of Style (16^{th} Edition):

Li, Chang. “Statistical Algorithms for Optimal Experimental Design with Correlated Observations.” 2013. Doctoral Dissertation, Utah State University. Accessed January 22, 2020. https://digitalcommons.usu.edu/etd/1507.

MLA Handbook (7^{th} Edition):

Li, Chang. “Statistical Algorithms for Optimal Experimental Design with Correlated Observations.” 2013. Web. 22 Jan 2020.

Vancouver:

Li C. Statistical Algorithms for Optimal Experimental Design with Correlated Observations. [Internet] [Doctoral dissertation]. Utah State University; 2013. [cited 2020 Jan 22]. Available from: https://digitalcommons.usu.edu/etd/1507.

Council of Science Editors:

Li C. Statistical Algorithms for Optimal Experimental Design with Correlated Observations. [Doctoral Dissertation]. Utah State University; 2013. Available from: https://digitalcommons.usu.edu/etd/1507

Utah State University

3. Moisen, Gretchen Gengenbach. Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA.

Degree: PhD, Mathematics and Statistics, 2000, Utah State University

URL: https://digitalcommons.usu.edu/etd/7108

Recent emphasis has been placed on merging regional forest inventory data with satellite-based information both to improve the efficiency of estimates of population totals, and to produce regional maps of forest variables. There are numerous ways in which forest class and structure variables may be modeled as functions of remotely sensed variables, yet surprisingly little work has been directed at surveying modem statistical techniques to determine which tools are best suited to the tasks given multiple objectives and logistical constraints. Here, a series of analyses to compare nonlinear and nonparametric modeling techniques for mapping a variety of forest variables, and for stratification of field plots, was conducted using data in the Interior Western United States. The analyses compared four statistical modeling techniques for predicting two discrete and four continuous forest inventory variables. The modeling techniques include generalized additive models (GAMs), classification and regression trees (CARTs), multivariate adaptive regression splines (MARS), and artificial neural networks (ANNs). Alternative stratification schemes were also compared for estimating population totals. The analyses were conducted within six ecologically different regions using a variety of satellite-based predictor variables. The work resulted in the development of an objective modeling box that automatically models spatial response variables as functions of any assortment of predictor variables through the four nonlinear or nonparametric modeling techniques. In comparing the different modeling techniques, all proved themselves workable in an automated environment, though ANNs were more problematic. When their potential mapping ability was explored through a simple simulation, tremendous advantages were seen in use of MARS and ANN for prediction over GAMs, CART, and a simple linear model. However, much smaller differences were seen when using real data. In some instances, a simple linear approach worked virtually as well as the more complex models, while small gains were seen using more complex models in other instances. In real data runs, MARS performed (marginally) best most often for binary variables, while GAMs performed (marginally) best most often for continuous variables. After considering a subjective "ease of use" measure, computing time and other predictive performance measures, it was determined that MARS had many advantages over other modeling techniques. In addition, stratification tests illustrated cost-effective means to improve precision of estimates of forest population totals. Finally, the general effect of map accuracy on the relative precision of estimates of population totals obtained under simple random sampling compared to that obtained under stratified random sampling was established and graphically illustrated as a tool for management decisions.
*Advisors/Committee Members: D. Richard Cutler, Joseph V. Koebbe, Daniel C. Coster, ;.*

Subjects/Keywords: nonlinear modeling techniques; nonparametric modeling techniques; mapping; stratification; forest inventories; Interior Western USA; Mathematics

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Moisen, G. G. (2000). Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7108

Chicago Manual of Style (16^{th} Edition):

Moisen, Gretchen Gengenbach. “Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA.” 2000. Doctoral Dissertation, Utah State University. Accessed January 22, 2020. https://digitalcommons.usu.edu/etd/7108.

MLA Handbook (7^{th} Edition):

Moisen, Gretchen Gengenbach. “Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA.” 2000. Web. 22 Jan 2020.

Vancouver:

Moisen GG. Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA. [Internet] [Doctoral dissertation]. Utah State University; 2000. [cited 2020 Jan 22]. Available from: https://digitalcommons.usu.edu/etd/7108.

Council of Science Editors:

Moisen GG. Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA. [Doctoral Dissertation]. Utah State University; 2000. Available from: https://digitalcommons.usu.edu/etd/7108