Resampling Techniques in Regression Analysis for Model Simplification: Model Selection Strategies - Softcover

Mahmood, Zafar

Softcover

ISBN 10: 3659142905 ISBN 13: 9783659142901

Publisher: LAP LAMBERT Academic Publishing, 2012

Synopsis

Resampling techniques are now-a-days widely used for model assessment and comparison. In the literature, many variable selection methods for regression modeling have been developed whose performance depends critically on the stopping rules. In this book, resampling application for variable selection on the basis of optimum choice of stopping rules for each data set and model simplification in various regression models are addressed. We propose a general approach of resampling techniques in regression analysis that allows us to ‘choose’ the stopping criterion's for each data set. Our selection method first choosing appropriate cutoff values/stopping criterion's and results in selecting a good subset regression model. We focus on optimizing cutoff values or stopping criterion's in automated model selection methods in regression analysis due to the interest in holding only authentic predictor variables in the regression models. We first focus on linear regression model, and then extended our approach to generalized regression, cox regression and finally robust regression.

About this title

About the Author

The author is faculty in the Department of Maths, Stats & C.Sc., KP Agricultural University. He is PhD in Statistics. He got his degree from University of Peshawar.He completed his research project in the Department of Biostatistics and Computational Biology, University of Rochester USA. He published 20 research papers and earn a research project

Edition details

• Publisher: LAP LAMBERT Academic Publishing
• Publication date: 2012
• Language: English
• ISBN 10: 3659142905
• ISBN 13: 9783659142901
• Binding: Paperback
• Number of pages: 272