Bayesian Variable Selection for High Dimensional Data Analysis

Language: English

Published by LAP LAMBERT Academic Publishing Sep 2011, 2011

3846505714 / 9783846505717

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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 92 pp. Englisch.

Seller Inventory # 9783846505717

Title
Bayesian Variable Selection for High Dimensional Data Analysis
Author
Yang Aijun
Publisher
LAP LAMBERT Academic Publishing Sep 2011
Publication year
2011
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3846505714
ISBN 13
9783846505717
Item weight
155 grams
Dimensions
220x150x6 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

Shipping rates from Germany to U.S.A.

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BuchWeltWeit Ludwig Meier e.K.

Germany