Publication Date: April 2011
Nonparametric additive modeling is a fundamental tool for statistical data analysis which allows flexible functional forms for conditional mean or quantile functions but avoids the curse of dimensionality for fully nonparametric methods induced by high-dimensional covariates. This paper proposes empirical likelihood-based inference methods for unknown functions in three types of nonparametric additive models: (i) additive mean regression with the identity link function, (ii) generalized additive mean regression with a known non-identity link function, and (iii) additive quantile regression. The proposed empirical likelihood ratio statistics for the unknown functions are asymptotically pivotal and converge to chi-square distributions, and their associated conﬁdence intervals possess several attractive features compared to the conventional Wald-type conﬁdence intervals.
Nonparametric additive model, Empirical likelihood, Generalized linear model, Quantile regression
JEL Classification Codes: C12, C14, C21, C25
Published in Journal of the Japan Statistical Society (2011), 41(2): 159-186 [DOI]