Document Type
Discussion Paper
Publication Date
4-1-2011
CFDP Number
1792
CFDP Pages
28
Abstract
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.
Recommended Citation
Otsu, Taisuke, "Empirical Likelihood for Nonparametric Additive Models" (2011). Cowles Foundation Discussion Papers. 2135.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/2135