Possibilistic Logistic Regression: in Fuzzy Environment - Couverture souple

Abdalla, Hesham A.

9783659263637: Possibilistic Logistic Regression: in Fuzzy Environment

Couverture souple

ISBN 10 : 365926363X ISBN 13 : 9783659263637

Editeur : LAP LAMBERT Academic Publishing, 2012

Afficher les exemplaires de cette �dition comportant l'ISBN

0 D'occasion

6 Neuf

De EUR 59

Parameter estimation for logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets ML estimation is a satisfactory approach. Unfortunately, ML may fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. This study extends logistic regression model to fuzzy logistic regression model by suggesting a new approach based on fuzzy concepts to estimate the model parameters. This study produces three proposed mathematical models with different objective functions. The first is formulated as a bi-objective programming model. The second is formulated using a goal programming approach, while the third is a mathematical programming model which minimizes the total spread of the estimated probabilities of the logistic model. The proposed models are evaluated and their results are compared to ML results through a Monte Carlo simulation study. The results are analyzed and summarized to conclude the following: The proposed models outperform ML approach for small size data sets with respect to the similarity measure as goodness of fit index.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

� propos de l'auteur

Dr hesham A. Abdalla is Assistant Professor, Department of Statistics and Insurance, Assiut University, Assiut, Egypt. He have a Ph.D. in Operation research-Cairo University,a Master in Applied Statistics-Cairo University, and a Bachelor of Science in Statistics-Cairo University

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2012
Langue: anglais
ISBN 10: 365926363X
ISBN 13: 9783659263637
Reliure: Broch�
Nombre de pages: 124
Coordonn�es du fabricant: non disponible
Personne responsable: KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Allemagne

R�sultats de recherche pour Possibilistic Logistic Regression: in Fuzzy Environment

Image fournie par le vendeur

Possibilistic Logistic Regression

Hesham A. Abdalla

Edit� par LAP LAMBERT Academic Publishing Okt 2012, 2012

ISBN 10 : 365926363X ISBN 13 : 9783659263637

Neuf Taschenbuch

impression � la demande

Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Parameter estimation for logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets ML estimation is a satisfactory approach. Unfortunately, ML may fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. This study extends logistic regression model to fuzzy logistic regression model by suggesting a new approach based on fuzzy concepts to estimate the model parameters. This study produces three proposed mathematical models with different objective functions. The first is formulated as a bi-objective programming model. The second is formulated using a goal programming approach, while the third is a mathematical programming model which minimizes the total spread of the estimated probabilities of the logistic model. The proposed models are evaluated and their results are compared to ML results through a Monte Carlo simulation study. The results are analyzed and summarized to conclude the following: The proposed models outperform ML approach for small size data sets with respect to the similarity measure as goodness of fit index 124 pp. Englisch. N� de r�f. du vendeur 9783659263637

Contacter le vendeur

Acheter neuf

EUR 59

Exp�dition �EUR 23
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 2 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Possibilistic Logistic Regression

Hesham A. Abdalla

Edit� par LAP LAMBERT Academic Publishing, 2012

ISBN 10 : 365926363X ISBN 13 : 9783659263637

Neuf Couverture souple

impression � la demande

Vendeur : moluna, Greven, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abdalla Hesham A.Dr hesham A. Abdalla is Assistant Professor, Department of Statistics and Insurance, Assiut University, Assiut, Egypt. He have a Ph.D. in Operation research-Cairo University,a Master in Applied Statistics-Cairo Unive. N� de r�f. du vendeur 5144097

Contacter le vendeur

Acheter neuf

EUR 48,50

Exp�dition �EUR 48,99
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image fournie par le vendeur

Possibilistic Logistic Regression

Hesham A. Abdalla

Edit� par LAP LAMBERT Academic Publishing Okt 2012, 2012

ISBN 10 : 365926363X ISBN 13 : 9783659263637

Neuf Taschenbuch

impression � la demande

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Parameter estimation for logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets ML estimation is a satisfactory approach. Unfortunately, ML may fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. This study extends logistic regression model to fuzzy logistic regression model by suggesting a new approach based on fuzzy concepts to estimate the model parameters. This study produces three proposed mathematical models with different objective functions. The first is formulated as a bi-objective programming model. The second is formulated using a goal programming approach, while the third is a mathematical programming model which minimizes the total spread of the estimated probabilities of the logistic model. The proposed models are evaluated and their results are compared to ML results through a Monte Carlo simulation study. The results are analyzed and summarized to conclude the following: The proposed models outperform ML approach for small size data sets with respect to the similarity measure as goodness of fit indexVDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 124 pp. Englisch. N� de r�f. du vendeur 9783659263637

Contacter le vendeur

Acheter neuf

EUR 59

Exp�dition �EUR 60
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 1 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Possibilistic Logistic Regression : in Fuzzy Environment

Hesham A. Abdalla

Edit� par LAP LAMBERT Academic Publishing, 2012

ISBN 10 : 365926363X ISBN 13 : 9783659263637

Neuf Taschenbuch

impression � la demande

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Parameter estimation for logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets ML estimation is a satisfactory approach. Unfortunately, ML may fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. This study extends logistic regression model to fuzzy logistic regression model by suggesting a new approach based on fuzzy concepts to estimate the model parameters. This study produces three proposed mathematical models with different objective functions. The first is formulated as a bi-objective programming model. The second is formulated using a goal programming approach, while the third is a mathematical programming model which minimizes the total spread of the estimated probabilities of the logistic model. The proposed models are evaluated and their results are compared to ML results through a Monte Carlo simulation study. The results are analyzed and summarized to conclude the following: The proposed models outperform ML approach for small size data sets with respect to the similarity measure as goodness of fit index. N� de r�f. du vendeur 9783659263637

Contacter le vendeur