This book is based on the important contributions in model selection by Dr. M. S. Rahman and his co-authors Professor M. L. King, Dr. G. K. Bose, Dr. M. R. Laskar and Mrs. S. Nahar. We have developed an analytical formula for finding the probability of correct selection. We have proposed a new criterion named JIC based on the combination of BIC and RBAR criteria and showed that JIC performed better in most cases than all existing criteria. We have introduced generalized criterion based on residual sum of squares with a multiplicative penalty function. We have proposed improved penalty functions for information criteria-based model selection which involves the use of computer simulation methods to improve the choice of penalty function. We have also examined the effect of making a restriction on parameters of interest within a model selection framework. We have studied the relation between model selection and hypothesis testing. Marginal penalty functions are derived for all criteria and used to compare the performances of all existing criteria. It is observed that in general RBAR criterion favors the higher parametric model and BIC favors the lower parametric model.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is based on the important contributions in model selection by Dr. M. S. Rahman and his co-authors Professor M. L. King, Dr. G. K. Bose, Dr. M. R. Laskar and Mrs. S. Nahar. We have developed an analytical formula for finding the probability of correct selection. We have proposed a new criterion named JIC based on the combination of BIC and RBAR criteria and showed that JIC performed better in most cases than all existing criteria. We have introduced generalized criterion based on residual sum of squares with a multiplicative penalty function. We have proposed improved penalty functions for information criteria-based model selection which involves the use of computer simulation methods to improve the choice of penalty function. We have also examined the effect of making a restriction on parameters of interest within a model selection framework. We have studied the relation between model selection and hypothesis testing. Marginal penalty functions are derived for all criteria and used to compare the performances of all existing criteria. It is observed that in general RBAR criterion favors the higher parametric model and BIC favors the lower parametric model. 164 pp. Englisch. N° de réf. du vendeur 9786200477071
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Vendeur : Books Puddle, New York, NY, Etats-Unis
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rahman M. ShafiqurDr. Shafiqur & Mrs. Syfun were born and brought up in Bangladesh and then migrated to Australia. He holds a PhD and she holds an M.Sc. in Statistics from Dalhousie Univ. of Canada. He has 40 years & She has 28 years. N° de réf. du vendeur 389368237
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is based on the important contributions in model selection by Dr. M. S. Rahman and his co-authors Professor M. L. King, Dr. G. K. Bose, Dr. M. R. Laskar and Mrs. S. Nahar. We have developed an analytical formula for finding the probability of correct selection. We have proposed a new criterion named JIC based on the combination of BIC and RBAR criteria and showed that JIC performed better in most cases than all existing criteria. We have introduced generalized criterion based on residual sum of squares with a multiplicative penalty function. We have proposed improved penalty functions for information criteria-based model selection which involves the use of computer simulation methods to improve the choice of penalty function. We have also examined the effect of making a restriction on parameters of interest within a model selection framework. We have studied the relation between model selection and hypothesis testing. Marginal penalty functions are derived for all criteria and used to compare the performances of all existing criteria. It is observed that in general RBAR criterion favors the higher parametric model and BIC favors the lower parametric model.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 164 pp. Englisch. N° de réf. du vendeur 9786200477071
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is based on the important contributions in model selection by Dr. M. S. Rahman and his co-authors Professor M. L. King, Dr. G. K. Bose, Dr. M. R. Laskar and Mrs. S. Nahar. We have developed an analytical formula for finding the probability of correct selection. We have proposed a new criterion named JIC based on the combination of BIC and RBAR criteria and showed that JIC performed better in most cases than all existing criteria. We have introduced generalized criterion based on residual sum of squares with a multiplicative penalty function. We have proposed improved penalty functions for information criteria-based model selection which involves the use of computer simulation methods to improve the choice of penalty function. We have also examined the effect of making a restriction on parameters of interest within a model selection framework. We have studied the relation between model selection and hypothesis testing. Marginal penalty functions are derived for all criteria and used to compare the performances of all existing criteria. It is observed that in general RBAR criterion favors the higher parametric model and BIC favors the lower parametric model. N° de réf. du vendeur 9786200477071
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Improved Model Selection Criteria | M. Shafiqur Rahman (u. a.) | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786200477071 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 118815435
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