Hesham a Abdalla (13 results)

Paperback. Condition: Brand New. 124 pages. 8.66x5.91x0.28 inches. In Stock.

Taschenbuch. Condition: Neu. Multinomial Logistic Regression with Fuzzy Parameters | Hesham A. Abdalla (u. a.) | Taschenbuch | 124 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659832550 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condition: Neu. Photocatalysis by Titania Nanocomposites | Synthesis and Characterization of Nano Titania and its Magnetic Composites for Photocatalysis | Hesham Ali Fahmy Abdalla Hamad (u. a.) | Taschenbuch | 332 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659544842 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condition: Neu. Possibilistic Logistic Regression | in Fuzzy Environment | Hesham A. Abdalla | Taschenbuch | 124 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659263637 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Two new developed multinomial logistic regression approach is proposed by incorporating the concepts of fuzzy sets. The first is formulated using a goal programming approach, while the second is formulated as a multi objective programming model. These two models are based on the assumption that the parameters are fuzzy. A simulation study is used to evaluate the suggested models comparing to the classical approach. Data are generated from different multinomial logistic models The design of the simulation study considers 40 different combinations of three factors. For each combination, a comparison between the performance of the proposed approach and ML approach is presented. 124 pp. Englisch.…

Taschenbuch. Condition: 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.…

Taschenbuch. Condition: 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.…

Condition: 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 has a Ph.D. in Operation Research-Cairo university.Dr Amany A. El-Sayed is Assistant Prof. of S.…

Condition: 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.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Two new developed multinomial logistic regression approach is proposed by incorporating the concepts of fuzzy sets. The first is formulated using a goal programming approach, while the second is formulated as a multi objective programming model. These two models are based on the assumption that the parameters are fuzzy. A simulation study is used to evaluate the suggested models comparing to the classical approach. Data are generated from different multinomial logistic models The design of the simulation study considers 40 different combinations of three factors. For each combination, a comparison between the performance of the proposed approach and ML approach is presented.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Two new developed multinomial logistic regression approach is proposed by incorporating the concepts of fuzzy sets. The first is formulated using a goal programming approach, while the second is formulated as a multi objective programming model. These two models are based on the assumption that the parameters are fuzzy. A simulation study is used to evaluate the suggested models comparing to the classical approach. Data are generated from different multinomial logistic models The design of the simulation study considers 40 different combinations of three factors. For each combination, a comparison between the performance of the proposed approach and ML approach is presented.…

Taschenbuch. Condition: 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 index 124 pp. Englisch.…