Bayesian Variable Selection for High Dimensional Data Analysis

Aijun Yang

ISBN 10: 3846505714 / ISBN 13: 9783846505717

Published by Editorial Academica Espanola, 2011

About this Item

Print on Demand pp. 92 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Seller Inventory # 95319182

Bibliographic Details

• Title: Bayesian Variable Selection for High Dimensional Data Analysis
• Publisher: Editorial Academica Espanola
• Publication Date: 2011
• Language: English
• ISBN 10: 3846505714
• ISBN 13: 9783846505717
• Binding: Soft cover
• Condition: New

About this title

Synopsis

In the practice of statistical modeling, it is often desirable to have an accurate predictive model. Modern data sets usually have a large number of predictors.Hence parsimony is especially an important issue. Best-subset selection is a conventional method of variable selection. Due to the large number of variables with relatively small sample size and severe collinearity among the variables, standard statistical methods for selecting relevant variables often face difficulties. Bayesian stochastic search variable selection has gained much empirical success in a variety of applications. This book, therefore, proposes a modified Bayesian stochastic variable selection approach for variable selection and two/multi-class classification based on a (multinomial) probit regression model.We demonstrate the performance of the approach via many real data. The results show that our approach selects smaller numbers of relevant variables and obtains competitive classification accuracy based on obtained results.

About the Author

Dr. Yang Aijun: Assiatant Professor and CFA, School of Finance, Nanjing Audit University; Ph.D, The Chinese University of Hong Kong. Yang's research interests include Stock Return Predictability, Portfolio Selection, Financial Risk Management and Variable Selection.