Semiparametric Additive Indices for Binary Response and Generalized Additive Models

Abstract

Models are studied where the response Y andcovariates X,T are assumed to fulfill E(Y | X;T) =G{XTbeta + alpha + m1(T1 ) + ...+ md(Td) }. Here G is a known (link) function,beta is an unknown parameter, and m1, ..., md areunknown functions. In particular, we consider additive binary response models where the response Y is binary. In these models, given X and T, the response Y has a Bernoulli distribution with parameter G{ XTbeta + alpha + m1(T1 ) + ... + md(Td) }. The paper discusses estimation of beta and m1, ... , md. Procedures are proposed for testing linearity of the additive components m1, ... , md. Furthermore, bootstrap uniform confidence intervals for the additive components are introduced. The practical performance of the proposed methods is discussed in simulations and in two economic applications.