封面圖片

Central double cross-validation for estimating parameters in regression models

The ridge regression, lasso, elastic net, forward stagewise regression and the least angle regression require a solution path and tuning parameter, λ, to estimate the coefficient vector. Therefore, it is crucial to find the ideal λ. Cross-validation (CV) is the most widely utilized method for choosi...

全面介紹

Saved in:
書目詳細資料
主要作者: Chye, Rou Shi
格式: Thesis
語言:English
出版: 2016
主題:
QA Mathematics
在線閱讀:http://eprints.utm.my/id/eprint/80959/2/ChyeRouShiMFS2016.pdf
http://eprints.utm.my/id/eprint/80959/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:120286
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
id my.utm.80959
record_format eprints
spelling my.utm.809592019-07-24T00:13:24Z http://eprints.utm.my/id/eprint/80959/ Central double cross-validation for estimating parameters in regression models Chye, Rou Shi QA Mathematics The ridge regression, lasso, elastic net, forward stagewise regression and the least angle regression require a solution path and tuning parameter, λ, to estimate the coefficient vector. Therefore, it is crucial to find the ideal λ. Cross-validation (CV) is the most widely utilized method for choosing the ideal tuning parameter from the solution path. CV is essentially the breaking down of the original sample into two parts. One part is used to develop the regression equation. The regression equation is then applied to the other part to evaluate the risk of every model. Consequently, the final model is the model with smallest estimated risk. However, CV does not provide consistent results because it has overfitting and underfitting effects during the model selection. In the present study, a new method for estimating parameter in best-subset regression called central double cross-validation (CDCV) is proposed. In this method, the CV is run twice with different number of folds. Therefore, CDCV maximizes the usage of available data, enhances the model selection performance and builds a new stable CV curve. The final model with an error of less than ?? standard error above the smallest CV error is chosen. The CDCV was compared to existing CV methods in determining the correct model via a simulation study with different sample size and correlation settings. Simulation study indicates that the proposed CDCV method has the highest percentage of obtaining the right model and the lowest Bayesian information criterion (BIC) value across multiple simulated study settings. The results showed that, CDCV has the ability to select the right model correctly and prevent the model from underfitting and overfitting. Therefore, CDCV is recommended as a good alternative to the existing methods in the simulation settings. 2016-07 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/80959/2/ChyeRouShiMFS2016.pdf Chye, Rou Shi (2016) Central double cross-validation for estimating parameters in regression models. Masters thesis, Universiti Teknologi Malaysia, Faculty of Science. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:120286
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Chye, Rou Shi
Central double cross-validation for estimating parameters in regression models
description The ridge regression, lasso, elastic net, forward stagewise regression and the least angle regression require a solution path and tuning parameter, λ, to estimate the coefficient vector. Therefore, it is crucial to find the ideal λ. Cross-validation (CV) is the most widely utilized method for choosing the ideal tuning parameter from the solution path. CV is essentially the breaking down of the original sample into two parts. One part is used to develop the regression equation. The regression equation is then applied to the other part to evaluate the risk of every model. Consequently, the final model is the model with smallest estimated risk. However, CV does not provide consistent results because it has overfitting and underfitting effects during the model selection. In the present study, a new method for estimating parameter in best-subset regression called central double cross-validation (CDCV) is proposed. In this method, the CV is run twice with different number of folds. Therefore, CDCV maximizes the usage of available data, enhances the model selection performance and builds a new stable CV curve. The final model with an error of less than ?? standard error above the smallest CV error is chosen. The CDCV was compared to existing CV methods in determining the correct model via a simulation study with different sample size and correlation settings. Simulation study indicates that the proposed CDCV method has the highest percentage of obtaining the right model and the lowest Bayesian information criterion (BIC) value across multiple simulated study settings. The results showed that, CDCV has the ability to select the right model correctly and prevent the model from underfitting and overfitting. Therefore, CDCV is recommended as a good alternative to the existing methods in the simulation settings.
format Thesis
author Chye, Rou Shi
author_facet Chye, Rou Shi
author_sort Chye, Rou Shi
title Central double cross-validation for estimating parameters in regression models
title_short Central double cross-validation for estimating parameters in regression models
title_full Central double cross-validation for estimating parameters in regression models
title_fullStr Central double cross-validation for estimating parameters in regression models
title_full_unstemmed Central double cross-validation for estimating parameters in regression models
title_sort central double cross-validation for estimating parameters in regression models
publishDate 2016
url http://eprints.utm.my/id/eprint/80959/2/ChyeRouShiMFS2016.pdf
http://eprints.utm.my/id/eprint/80959/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:120286
_version_ 1643658567577763840
score 13.251813

相似書籍