Hybrid combinations of parametric and empirical likelihoods

TL;DR

This paper introduces a hybrid likelihood method combining parametric and empirical likelihoods to improve inference robustness and efficiency, with theoretical guarantees and practical parameter selection strategies.

Contribution

It develops a new hybrid likelihood framework that balances parametric and empirical likelihoods, providing asymptotic properties and robustness under model misspecification.

Findings

Asymptotic normality of the hybrid likelihood estimator

Wilks-type theorem for the hybrid likelihood

Methods for selecting the compromise parameter a

Abstract

This paper develops a hybrid likelihood (HL) method based on a compromise between parametric and nonparametric likelihoods. Consider the setting of a parametric model for the distribution of an observation $Y$ with parameter $\theta$. Suppose there is also an estimating function $m(\cdot,\mu)$ identifying another parameter $\mu$ via $E\,m(Y,\mu)=0$, at the outset defined independently of the parametric model. To borrow strength from the parametric model while obtaining a degree of robustness from the empirical likelihood method, we formulate inference about $\theta$ in terms of the hybrid likelihood function $H_n(\theta)=L_n(\theta)^{1-a}R_n(\mu(\theta))^a$. Here $a\in[0,1)$ represents the extent of the compromise, $L_n$ is the ordinary parametric likelihood for $\theta$, $R_n$ is the empirical likelihood function, and $\mu$ is considered through the lens of the parametric model. We establish asymptotic normality of the corresponding HL estimator and a version of the Wilks theorem. We also examine extensions of these results under misspecification of the parametric model, and propose methods for selecting the balance parameter $a$.