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Bayesian Variable Selection for High Dimensional Data Analysis: methods and Applications - Softcover
Inhaltsangabe
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.
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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.
Biografía del autor
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.
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Bayesian Variable Selection for High Dimensional Data Analysis
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
Neu
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Zustand: 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. Bestandsnummer des Verkäufers 5495281
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Bayesian Variable Selection for High Dimensional Data Analysis
Verlag:
LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
Neu
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783846505717
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Bayesian Variable Selection for High Dimensional Data Analysis : methods and Applications
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783846505717
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Bayesian Variable Selection for High Dimensional Data Analysis
Verlag:
LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783846505717
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Bayesian Variable Selection for High Dimensional Data Analysis
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Bayesian Variable Selection for High Dimensional Data Analysis
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Bayesian Variable Selection for High Dimensional Data Analysis: methods and Applications
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846505714
ISBN 13: 9783846505717
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