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You searched for +publisher:"Utah State University" +contributor:("D. Richard Cutler"). Showing records 1 – 3 of 3 total matches.

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Utah State University

1. Lundell, Jill F. Tuning Hyperparameters in Supervised Learning Models and Applications of Statistical Learning in Genome-Wide Association Studies with Emphasis on Heritability.

Degree: PhD, Mathematics and Statistics, 2019, Utah State University

Machine learning is a buzz word that has inundated popular culture in the last few years. This is a term for a computer method that can automatically learn and improve from data instead of being explicitly programmed at every step. Investigations regarding the best way to create and use these methods are prevalent in research. Machine learning models can be difficult to create because models need to be tuned. This dissertation explores the characteristics of tuning three popular machine learning models and finds a way to automatically select a set of tuning parameters. This information was used to create an R software package called EZtune that can be used to automatically tune three widely used machine learning algorithms: support vector machines, gradient boosting machines, and adaboost. The second portion of this dissertation investigates the implementation of machine learning methods in finding locations along a genome that are associated with a trait. The performance of methods that have been commonly used for these types of studies, and some that have not been commonly used, are assessed using simulated data. The affect of the strength of the relationship between the genetic code and the trait is of particular interest. It was found that the strength of this relationship was the most important characteristic in the efficacy of each method. Advisors/Committee Members: D. Richard Cutler, Chris D. Corcoran, Adele Cutler, ;.

Subjects/Keywords: tuning; genetics; statistical learning; Statistics and Probability

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

APA (6th Edition):

Lundell, J. F. (2019). Tuning Hyperparameters in Supervised Learning Models and Applications of Statistical Learning in Genome-Wide Association Studies with Emphasis on Heritability. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7594

Chicago Manual of Style (16th Edition):

Lundell, Jill F. “Tuning Hyperparameters in Supervised Learning Models and Applications of Statistical Learning in Genome-Wide Association Studies with Emphasis on Heritability.” 2019. Doctoral Dissertation, Utah State University. Accessed December 08, 2019. https://digitalcommons.usu.edu/etd/7594.

MLA Handbook (7th Edition):

Lundell, Jill F. “Tuning Hyperparameters in Supervised Learning Models and Applications of Statistical Learning in Genome-Wide Association Studies with Emphasis on Heritability.” 2019. Web. 08 Dec 2019.

Vancouver:

Lundell JF. Tuning Hyperparameters in Supervised Learning Models and Applications of Statistical Learning in Genome-Wide Association Studies with Emphasis on Heritability. [Internet] [Doctoral dissertation]. Utah State University; 2019. [cited 2019 Dec 08]. Available from: https://digitalcommons.usu.edu/etd/7594.

Council of Science Editors:

Lundell JF. Tuning Hyperparameters in Supervised Learning Models and Applications of Statistical Learning in Genome-Wide Association Studies with Emphasis on Heritability. [Doctoral Dissertation]. Utah State University; 2019. Available from: https://digitalcommons.usu.edu/etd/7594


Utah State University

2. Merrill, Andrew C. Investigations of Variable Importance Measures Within Random Forests.

Degree: MS, Mathematics and Statistics, 2009, Utah State University

Random Forests (RF) (Breiman 2001; Breiman and Cutler 2004) is a completely nonparametric statistical learning procedure that may be used for regression analysis and. A feature of RF that is drawing a lot of attention is the novel algorithm that is used to evaluate the relative importance of the predictor/explanatory variables. Other machine learning algorithms for regression and classification, such as support vector machines and artificial neural networks (Hastie et al. 2009), exhibit high predictive accuracy but provide little insight into predictive power of individual variables. In contrast, the permutation algorithm of RF has already established a track record for identification of important predictors (Huang et al. 2005; Cutler et al. 2007; Archer and Kimes 2008). Recently, however, some authors (Nicodemus and Shugart 2007; Strobl et al. 2007, 2008) have shown that the presence of categorical variables with many categories (Strobl et al. 2007) or high colinearity give unduly large variable importance using the standard RF permutation algorithm (Strobl et al. 2008). This work creates simulations from multiple linear regression models with small numbers of variables to understand the issues raised by Strobl et al. (2008) regarding shortcomings of the original RF variable importance algorithm and the alternatives implemented in conditional forests (Strobl et al. 2008). In addition this paper will look at the dependence of RF variable importance values on user-defined parameters. Advisors/Committee Members: D. Richard Cutler, ;.

Subjects/Keywords: investigations; variable; measures; random; forests; Statistics and Probability

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

APA (6th Edition):

Merrill, A. C. (2009). Investigations of Variable Importance Measures Within Random Forests. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7078

Chicago Manual of Style (16th Edition):

Merrill, Andrew C. “Investigations of Variable Importance Measures Within Random Forests.” 2009. Masters Thesis, Utah State University. Accessed December 08, 2019. https://digitalcommons.usu.edu/etd/7078.

MLA Handbook (7th Edition):

Merrill, Andrew C. “Investigations of Variable Importance Measures Within Random Forests.” 2009. Web. 08 Dec 2019.

Vancouver:

Merrill AC. Investigations of Variable Importance Measures Within Random Forests. [Internet] [Masters thesis]. Utah State University; 2009. [cited 2019 Dec 08]. Available from: https://digitalcommons.usu.edu/etd/7078.

Council of Science Editors:

Merrill AC. Investigations of Variable Importance Measures Within Random Forests. [Masters Thesis]. Utah State University; 2009. Available from: https://digitalcommons.usu.edu/etd/7078


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

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

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

APA (6th 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 (16th 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 December 08, 2019. https://digitalcommons.usu.edu/etd/7108.

MLA Handbook (7th Edition):

Moisen, Gretchen Gengenbach. “Comparing Nonlinear and Nonparametric Modeling Techniques for Mapping and Stratification in Forest Inventories of the Interior Western USA.” 2000. Web. 08 Dec 2019.

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 2019 Dec 08]. 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

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