Magda Haggag - Academia.edu (original) (raw)

Papers by Magda Haggag

Research paper thumbnail of Difference for Difference Estimation Method for Semiparametric Partially Linear Regression Models

Social Science Research Network, Oct 31, 2019

This paper proposes a new difference-based estimation method for estimating the semi parametric p... more This paper proposes a new difference-based estimation method for estimating the semi parametric partially linear model (PLM). This method is called the difference for difference (DFD) estimation method, which is proposed by the author, for estimating the residual variance in nonparametric regression models. In this work, the DFD estimation method is used for estimating both the parametric component and the residual variance. A numerical study has been shown that the proposed DFD estimation gives best results compared to other existing difference methods; in the form of less mean squared error of parametric component and less residual variance of the fitted model.

Research paper thumbnail of On The Estimation of a Semiparametric Generalized Linear Model

In this article, estimation methods of the semiparametric generalized linear model known as the g... more In this article, estimation methods of the semiparametric generalized linear model known as the generalized partial linear model (GPLM) are reviewed. These methods are based on using kernel smoothing functions in the estimation of the nonparametric component of the model. We derive the algorithms for the estimation process and develop these algorithms for the generalized partial linear model (GPLM) with a binary response.

Research paper thumbnail of On The Estimation of a Semiparametric Generalized Linear Model BY

In this article, estimation methods of the semiparametric generalized linear model known as the g... more In this article, estimation methods of the semiparametric generalized linear model known as the generalized partial linear model (GPLM) are reviewed. These methods are based on using kernel smoothing functions in the estimation of the nonparametric component of the model. We derive the algorithms for the estimation process and develop these algorithms for the generalized partial linear model (GPLM) with a binary response.

Research paper thumbnail of Estimation of Parametric and Semiparametric Logistic Regression Models Using Credit Scoring Data

In this paper, a semiparametric generalized linear model known as a generalized partial linear mo... more In this paper, a semiparametric generalized linear model known as a generalized partial linear model (GPLM) is estimated using the profile likelihood method. The Algorithms of the estimation process is derived and applied to credit scoring data. Credit scoring data is a typical example for the application of GPLM with a binomially distributed response variable. Key Wards: credit scoring, generalized linear model, generalized partial linear model, misclassification rates, profile-likelihood, quasi-likelihood, semiparametric estimation.

Research paper thumbnail of Adjusting the penalized term for the regularized regression models

More attention has been given to regularization methods in the last two decades as a result of ex... more More attention has been given to regularization methods in the last two decades as a result of exiting high-dimensional ill-posed data. This paper proposes a new method of introducing the penalized term in regularized regression. The proposed penalty is based on using the least squares estimator’s variances of the regression parameters. The proposed method is applied to some penalized estimators like ridge, lasso, and elastic net, which are used to overcome both the multicollinearity problem and selecting variables. Good results are obtained using the average mean squared error criterion (AMSE) for simulated data, also real data are shown best results in the form of less average prediction errors (APE) of the resulting estimators. Keywords: Elastic-Net, Lasso, Penalized regression; Regularization; Ridge regression; Shrinkage; Variable selection AMS 2010 Mathematics Subject Classification : 62J05; 62J07

Research paper thumbnail of Estimation of Parametric and Semiparametric Logistic Regression Models Using Credit Scoring Data

In this paper, a semiparametric generalized linear model known as a generalized partial linear mo... more In this paper, a semiparametric generalized linear model known as a generalized partial linear model (GPLM) is estimated using the profile likelihood method. The Algorithms of the estimation process is derived and applied to credit scoring data. Credit scoring data is a typical example for the application of GPLM with a binomially distributed response variable.

Research paper thumbnail of Theory and Practice of Mathematics and Computer Science Vol. 2

Research paper thumbnail of Adaptive Partially Linear Regression Models by Mixing Different Estimates

American Journal of Theoretical and Applied Statistics

This paper proposes adapting the semiparametric partial model (PLM) by mixing different estimatio... more This paper proposes adapting the semiparametric partial model (PLM) by mixing different estimation procedures defined under different conditions. Choosing an estimation method of PLM, from several estimation methods, is an important issue, which depends on the performance of the method and the properties of the resulting estimators. Practically, it is difficult to assign the conditions which give the best estimation procedure for the data at hand, so adaptive procedure is needed. Kernel smoothing, spline smoothing, and difference based methods are different estimation procedures used to estimate the partially linear model. Some of these methods will be used in adapting the PLM by mixing. The adapted proposed estimator is found to be a square root-consistent and has asymptotic normal distribution for the parametric component of the model. Simulation studies with different settings, and real data are used to evaluate the proposed adaptive estimator. Correlated and non-correlated regressors are used for the parametric components of the semiparametric partial model (PLM). Best results are obtained in the case of correlated regressors than in the non-correlated ones. The proposed adaptive estimator is compared to the candidate model estimators used in mixing. Best results are obtained in the form of less risk error and less convergence rate for the proposed adaptive partial linear model (PLM).

Research paper thumbnail of A New Difference Method for Variance Estimation in Non-Parametric Regression Models

Advances and Applications in Statistics

Research paper thumbnail of A New Fuzzy Regression Model by Mixing Fuzzy and Crisp Inputs

AMERICAN REVIEW OF MATHEMATICS AND STATISTICS

This paper proposes a new form of the multiple regression model (mixed model) based on adding bot... more This paper proposes a new form of the multiple regression model (mixed model) based on adding both fuzzy and crisp input data. The least squares approach of the proposed multiple regression parameters are derived in different cases. This derivation is based on the fact that each fuzzy datum is a nonempty compact interval of the real line. The main contribution is to mix both fuzzy and crisp predictors in the linear regression model. The mixed fuzzy crisp model will be introduced mathematically and by coded via R-language. The least squares of the regression parameters will be derived and evaluated using distance measures. Numerical examples using generated data showed best results for the mixed fuzzy crisp multiple regression models compared to the multiple fuzzy models.

Research paper thumbnail of On The Estimation of a Semiparametric Generalized Linear Model

In this article, estimation methods of the semiparametric generalized linear model known as the g... more In this article, estimation methods of the semiparametric generalized linear model known as the generalized partial linear model (GPLM) are reviewed. These methods are based on using kernel smoothing functions in the estimation of the nonparametric component of the model. We derive the algorithms for the estimation process and develop these algorithms for the generalized partial linear model (GPLM) with

Research paper thumbnail of New Criteria of Model Selection and Model Averaging in Linear Regression Models

American Journal of Theoretical and Applied Statistics, 2014

Model selection is an important part of any statistical analysis. Many tools are suggested for se... more Model selection is an important part of any statistical analysis. Many tools are suggested for selecting the best model including frequentist and Bayesian perspectives. There is often a considerable uncertainty in the selection of a particular model to be the best approximating model. Model selection uncertainty arises when the data are used for both model selection and parameter estimation. Bias in estimators of model parameters often arise when data based selection has been done. Therefore, model averaging of the parameter estimators will be done to alleviate the bias in model selection in a set of candidate models, by combining the information from a set of candidate models. This paper is twofold , new criteria of model selection are proposed based on different averages of AIC, BIC, AICc, and HQC. Also, model averaging is introduced to compare the parameter estimators in model averaging with the ones in model selection. Two Simulation studies are considered, the first is for model selection and showed that the new proposed criteria are lies between some of the known criteria such as AIC, BIC, AICc, and HQC, and so they can be used as new criteria of model selection. The second simulation study is for model averaging and showed that the parameter estimators have less bias and less predicted mean square error (PMSE) compared with the parameter estimators in model selection.

Research paper thumbnail of ANALYSIS OF MULTIPLE LINEAR REGRESSION MODELS USING SYMBOLIC INTERVAL-VALUED VARIABLES

In this paper, a proposed form of dependent regression models are introduced to study symbolic da... more In this paper, a proposed form of dependent regression models are introduced to study symbolic data. The estimation of the proposed linear regression models are based on interval valued data, for which we have lower and upper bounds or center and range values. The least squares method is used to estimate the models. A real example data are used to illustrate the usefulness of the proposed regression models for handling the interval valued data. The estimation results are evaluated using the predicted mean squared errors. The results support the proposed dependent regression models.

Research paper thumbnail of Difference for Difference Estimation Method for Semiparametric Partially Linear Regression Models

Social Science Research Network, Oct 31, 2019

This paper proposes a new difference-based estimation method for estimating the semi parametric p... more This paper proposes a new difference-based estimation method for estimating the semi parametric partially linear model (PLM). This method is called the difference for difference (DFD) estimation method, which is proposed by the author, for estimating the residual variance in nonparametric regression models. In this work, the DFD estimation method is used for estimating both the parametric component and the residual variance. A numerical study has been shown that the proposed DFD estimation gives best results compared to other existing difference methods; in the form of less mean squared error of parametric component and less residual variance of the fitted model.

Research paper thumbnail of On The Estimation of a Semiparametric Generalized Linear Model

In this article, estimation methods of the semiparametric generalized linear model known as the g... more In this article, estimation methods of the semiparametric generalized linear model known as the generalized partial linear model (GPLM) are reviewed. These methods are based on using kernel smoothing functions in the estimation of the nonparametric component of the model. We derive the algorithms for the estimation process and develop these algorithms for the generalized partial linear model (GPLM) with a binary response.

Research paper thumbnail of On The Estimation of a Semiparametric Generalized Linear Model BY

In this article, estimation methods of the semiparametric generalized linear model known as the g... more In this article, estimation methods of the semiparametric generalized linear model known as the generalized partial linear model (GPLM) are reviewed. These methods are based on using kernel smoothing functions in the estimation of the nonparametric component of the model. We derive the algorithms for the estimation process and develop these algorithms for the generalized partial linear model (GPLM) with a binary response.

Research paper thumbnail of Estimation of Parametric and Semiparametric Logistic Regression Models Using Credit Scoring Data

In this paper, a semiparametric generalized linear model known as a generalized partial linear mo... more In this paper, a semiparametric generalized linear model known as a generalized partial linear model (GPLM) is estimated using the profile likelihood method. The Algorithms of the estimation process is derived and applied to credit scoring data. Credit scoring data is a typical example for the application of GPLM with a binomially distributed response variable. Key Wards: credit scoring, generalized linear model, generalized partial linear model, misclassification rates, profile-likelihood, quasi-likelihood, semiparametric estimation.

Research paper thumbnail of Adjusting the penalized term for the regularized regression models

More attention has been given to regularization methods in the last two decades as a result of ex... more More attention has been given to regularization methods in the last two decades as a result of exiting high-dimensional ill-posed data. This paper proposes a new method of introducing the penalized term in regularized regression. The proposed penalty is based on using the least squares estimator’s variances of the regression parameters. The proposed method is applied to some penalized estimators like ridge, lasso, and elastic net, which are used to overcome both the multicollinearity problem and selecting variables. Good results are obtained using the average mean squared error criterion (AMSE) for simulated data, also real data are shown best results in the form of less average prediction errors (APE) of the resulting estimators. Keywords: Elastic-Net, Lasso, Penalized regression; Regularization; Ridge regression; Shrinkage; Variable selection AMS 2010 Mathematics Subject Classification : 62J05; 62J07

Research paper thumbnail of Estimation of Parametric and Semiparametric Logistic Regression Models Using Credit Scoring Data

In this paper, a semiparametric generalized linear model known as a generalized partial linear mo... more In this paper, a semiparametric generalized linear model known as a generalized partial linear model (GPLM) is estimated using the profile likelihood method. The Algorithms of the estimation process is derived and applied to credit scoring data. Credit scoring data is a typical example for the application of GPLM with a binomially distributed response variable.

Research paper thumbnail of Theory and Practice of Mathematics and Computer Science Vol. 2

Research paper thumbnail of Adaptive Partially Linear Regression Models by Mixing Different Estimates

American Journal of Theoretical and Applied Statistics

This paper proposes adapting the semiparametric partial model (PLM) by mixing different estimatio... more This paper proposes adapting the semiparametric partial model (PLM) by mixing different estimation procedures defined under different conditions. Choosing an estimation method of PLM, from several estimation methods, is an important issue, which depends on the performance of the method and the properties of the resulting estimators. Practically, it is difficult to assign the conditions which give the best estimation procedure for the data at hand, so adaptive procedure is needed. Kernel smoothing, spline smoothing, and difference based methods are different estimation procedures used to estimate the partially linear model. Some of these methods will be used in adapting the PLM by mixing. The adapted proposed estimator is found to be a square root-consistent and has asymptotic normal distribution for the parametric component of the model. Simulation studies with different settings, and real data are used to evaluate the proposed adaptive estimator. Correlated and non-correlated regressors are used for the parametric components of the semiparametric partial model (PLM). Best results are obtained in the case of correlated regressors than in the non-correlated ones. The proposed adaptive estimator is compared to the candidate model estimators used in mixing. Best results are obtained in the form of less risk error and less convergence rate for the proposed adaptive partial linear model (PLM).

Research paper thumbnail of A New Difference Method for Variance Estimation in Non-Parametric Regression Models

Advances and Applications in Statistics

Research paper thumbnail of A New Fuzzy Regression Model by Mixing Fuzzy and Crisp Inputs

AMERICAN REVIEW OF MATHEMATICS AND STATISTICS

This paper proposes a new form of the multiple regression model (mixed model) based on adding bot... more This paper proposes a new form of the multiple regression model (mixed model) based on adding both fuzzy and crisp input data. The least squares approach of the proposed multiple regression parameters are derived in different cases. This derivation is based on the fact that each fuzzy datum is a nonempty compact interval of the real line. The main contribution is to mix both fuzzy and crisp predictors in the linear regression model. The mixed fuzzy crisp model will be introduced mathematically and by coded via R-language. The least squares of the regression parameters will be derived and evaluated using distance measures. Numerical examples using generated data showed best results for the mixed fuzzy crisp multiple regression models compared to the multiple fuzzy models.

Research paper thumbnail of On The Estimation of a Semiparametric Generalized Linear Model

In this article, estimation methods of the semiparametric generalized linear model known as the g... more In this article, estimation methods of the semiparametric generalized linear model known as the generalized partial linear model (GPLM) are reviewed. These methods are based on using kernel smoothing functions in the estimation of the nonparametric component of the model. We derive the algorithms for the estimation process and develop these algorithms for the generalized partial linear model (GPLM) with

Research paper thumbnail of New Criteria of Model Selection and Model Averaging in Linear Regression Models

American Journal of Theoretical and Applied Statistics, 2014

Model selection is an important part of any statistical analysis. Many tools are suggested for se... more Model selection is an important part of any statistical analysis. Many tools are suggested for selecting the best model including frequentist and Bayesian perspectives. There is often a considerable uncertainty in the selection of a particular model to be the best approximating model. Model selection uncertainty arises when the data are used for both model selection and parameter estimation. Bias in estimators of model parameters often arise when data based selection has been done. Therefore, model averaging of the parameter estimators will be done to alleviate the bias in model selection in a set of candidate models, by combining the information from a set of candidate models. This paper is twofold , new criteria of model selection are proposed based on different averages of AIC, BIC, AICc, and HQC. Also, model averaging is introduced to compare the parameter estimators in model averaging with the ones in model selection. Two Simulation studies are considered, the first is for model selection and showed that the new proposed criteria are lies between some of the known criteria such as AIC, BIC, AICc, and HQC, and so they can be used as new criteria of model selection. The second simulation study is for model averaging and showed that the parameter estimators have less bias and less predicted mean square error (PMSE) compared with the parameter estimators in model selection.

Research paper thumbnail of ANALYSIS OF MULTIPLE LINEAR REGRESSION MODELS USING SYMBOLIC INTERVAL-VALUED VARIABLES

In this paper, a proposed form of dependent regression models are introduced to study symbolic da... more In this paper, a proposed form of dependent regression models are introduced to study symbolic data. The estimation of the proposed linear regression models are based on interval valued data, for which we have lower and upper bounds or center and range values. The least squares method is used to estimate the models. A real example data are used to illustrate the usefulness of the proposed regression models for handling the interval valued data. The estimation results are evaluated using the predicted mean squared errors. The results support the proposed dependent regression models.