Empirical Likelihood for Nonparametric Additive Models

Taisuke Otsu
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Taisuke Otsu: Cowles Foundation, Yale University, https://cowles.yale.edu/

No 1792, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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.

Keywords: Nonparametric additive model; Empirical likelihood; Generalized linear model; Quantile regression (search for similar items in EconPapers)
JEL-codes: C12 C14 C21 C25 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2011-04
New Economics Papers: this item is included in nep-ecm and nep-ore
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