Using Generalized Additive Partial Linear Model for Constructing Underwriting System

Estimation and Variable Selection for Generalized Additive Partial Linear Models.

We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus ...

Generalized Additive Partial Linear Models for Clustered Data with Diverging Number of Covariates Using Gee

We study flexible modeling of clustered data using marginal generalized additive partial linear models with a diverging number of covariates. Generalized estimating equations are used to fit the model with the nonparametric functions being approximated by polynomial splines. We investigate the asymptotic properties in a “large n, diverging p” framework. More specifically, we establish the consi...

Computations for Linear and Generalized Additive Models

Generalized Additive and Generalized Linear Modeling for Children Diseases

This paper is necessarily restricted to application of Generalised Linear Models(GLM) and Generalised Additive Models(GAM), and is intended to provide readers with some measure of the power of these mathematical tools for modeling Health/Illness data systems. We are all aware that illness, in general and children illness, in particular is amongst the most serious socio-economic and demographic ...

KernGPLM – A Package for Kernel-Based Fitting of Generalized Partial Linear and Additive Models

In many cases statisticians are not only required to provide optimal fits or classification results but also to interpret and visualize the fitted curves or discriminant rules. A main issue here is to explain in what way the explanatory variables impact the resulting fit. The R package KernGPLM (currently under development) implements semiparametric extensions to the generalized linear regressi...