Fitting Generalized Additive Logistic Regression Model with GAM Procedure

作     者：Suresh Kumar Sharma Rashmi Aggarwal Kanchan Jain 

作者机构：Department of Statistics Panjab University Chandigarh India 

出 版 物：《Journal of Mathematics and System Science》 (数学和系统科学（英文版）)

年 卷 期：2013年第3卷第9期

页      面：442-453页

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0835[工学-软件工程] 070103[理学-概率论与数理统计] 0701[理学-数学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Logistic model iterative generalized additive model weighted least squares cubic splines. 

摘      要：In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.