On The Estimation of a Semiparametric Generalized Linear Model BY * Magda M. M. Haggag Abstract 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. Key Wards: Backfitting estimator, generalized linear model, generalized partial linear model, kernel smoothing, profile-likelihood, quasi-likelihood, semiparametric estimation, Speckman estimator. 1. Introduction A semiparametric generalized linear model known as a generalized partial linear model (GPLM) is an extension of a generalized linear model (GLM), ( ) ( ) β T Z G Z Y E = \ , (1) where G in a known and monotone link function, and β in an unknown finite ________________________________________________________________ *Department of Statistics, Mathematics, and Insurance, Faculty of Commerce, Alexandria University (Damanhoor branch), Egypt.