Articles liés à Bayesian Variable Selection for High Dimensional Data...
Bayesian Variable Selection for High Dimensional Data Analysis: methods and Applications - Couverture souple
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Présentation de l'éditeur
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.
Biographie de l'auteur
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 41,05
EUR 9,70 expédition depuis Allemagne vers France
Destinations, frais et délaisRésultats de recherche pour Bayesian Variable Selection for High Dimensional Data...
Image fournie par le vendeur
Bayesian Variable Selection for High Dimensional Data Analysis
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3846505714
ISBN 13 : 9783846505717
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : 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. N° de réf. du vendeur 5495281
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Bayesian Variable Selection for High Dimensional Data Analysis : methods and Applications
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3846505714
ISBN 13 : 9783846505717
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9783846505717
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Bayesian Variable Selection for High Dimensional Data Analysis
Edité par
LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10 : 3846505714
ISBN 13 : 9783846505717
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9783846505717
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Bayesian Variable Selection for High Dimensional Data Analysis
Edité par
LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10 : 3846505714
ISBN 13 : 9783846505717
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9783846505717
Quantité disponible : 2 disponible(s)
Image d'archives
Bayesian Variable Selection for High Dimensional Data Analysis
Edité par
Editorial Academica Espanola, 2011
ISBN 10 : 3846505714
ISBN 13 : 9783846505717
Neuf
Couverture souple
Vendeur : Books Puddle, New York, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. pp. 92. N° de réf. du vendeur 2698159441
Quantité disponible : 4 disponible(s)
Image d'archives
Bayesian Variable Selection for High Dimensional Data Analysis
Edité par
Editorial Academica Espanola, 2011
ISBN 10 : 3846505714
ISBN 13 : 9783846505717
Neuf
Couverture souple
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. PRINT ON DEMAND pp. 92. N° de réf. du vendeur 1898159451
Quantité disponible : 4 disponible(s)
Image d'archives
Bayesian Variable Selection for High Dimensional Data Analysis
Edité par
Editorial Academica Espanola, 2011
ISBN 10 : 3846505714
ISBN 13 : 9783846505717
Neuf
Couverture souple
Vendeur : Majestic Books, Hounslow, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : 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. N° de réf. du vendeur 95319182
Quantité disponible : 4 disponible(s)
Image d'archives
Bayesian Variable Selection for High Dimensional Data Analysis: methods and Applications
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 3846505714
ISBN 13 : 9783846505717
Ancien ou d'occasion
Paperback
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
Paperback. Etat : Like New. Like New. book. N° de réf. du vendeur ERICA79638465057146
Quantité disponible : 1 disponible(s)