Bayesian Variable Selection for High Dimensional Data Analysis: methods and Applications - Softcover
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.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
Search results for Bayesian Variable Selection for High Dimensional Data...
Seller Image
Bayesian Variable Selection for High Dimensional Data Analysis
Published by
LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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
Buy New
US$ 59.12
US$ 26.94 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 2 available
Stock Image
Bayesian Variable Selection for High Dimensional Data Analysis
Published by
Editorial Academica Espanola, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
New
Softcover
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. pp. 92. Seller Inventory # 2698159441
Stock Image
Bayesian Variable Selection for High Dimensional Data Analysis
Published by
Editorial Academica Espanola, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
New
Softcover
Seller: Majestic Books, Hounslow, United Kingdom
Seller rating 4 out of 5 stars
Condition: New. 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
Buy New
US$ 85.89
US$ 8.80 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 4 available
Stock Image
Bayesian Variable Selection for High Dimensional Data Analysis
Published by
Editorial Academica Espanola, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
New
Softcover
Seller: Biblios, Frankfurt am main, HESSE, Germany
Seller rating 4 out of 5 stars
Condition: New. PRINT ON DEMAND pp. 92. Seller Inventory # 1898159451
Buy New
US$ 91.94
US$ 11.66 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 4 available
Seller Image
Bayesian Variable Selection for High Dimensional Data Analysis
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 4 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Aijun YangDr. 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, Financ. Seller Inventory # 5495281
Buy New
US$ 49.53
US$ 57.39 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: Over 20 available
Seller Image
Bayesian Variable Selection for High Dimensional Data Analysis
Published by
LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 92 pp. Englisch. Seller Inventory # 9783846505717
Buy New
US$ 59.12
US$ 70.29 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 2 available
Seller Image
Bayesian Variable Selection for High Dimensional Data Analysis : methods and Applications
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783846505717
Buy New
US$ 59.12
US$ 71.20 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 1 available
Seller Image
Bayesian Variable Selection for High Dimensional Data Analysis | methods and Applications
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
New
Taschenbuch
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Bayesian Variable Selection for High Dimensional Data Analysis | methods and Applications | Yang Aijun | Taschenbuch | 92 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846505717 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 106795362
Buy New
US$ 52.25
US$ 82.00 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 5 available
Stock Image
Bayesian Variable Selection for High Dimensional Data Analysis: methods and Applications
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
Used
Paperback
Seller: Mispah books, Redhill, SURRE, United Kingdom
Seller rating 4 out of 5 stars
Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Seller Inventory # ERICA79638465057146
Buy Used
US$ 133.81
US$ 33.83 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available