Abstract: In many applications, we collect independent samples from interconnected populations. These population distributions share some latent structure, so it is advantageous to jointly analyze the multiple samples. Recently, many researchers have advocated the use of the semiparametric density ratio model (DRM) to account for the latent structure the multiple populations share and have developed more efficient data analysis procedures based on pooled data. In this talk, we investigate the efficiency of some estimators under a two-sample DRM. We consider the scenario where we have two samples whose sizes grow to infinity at different rates, and study the DRM-based inferences for the population corresponding to the smaller-sized sample. We theoretically prove that some DRM-based estimators achieve the same asymptotic efficiency as the parametric estimates derived under a specific parametric model. Our simulation studies on quantile estimation help to confirm our theoretical results.

Archer Gong Zhang

Posts by Collection

portfolio

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publications

Empirical likelihood ratio test on quantiles under a density ratio model

Semiparametric inferences under a density ratio model

Density ratio model with data-adaptive basis function

talks

Empirical Likelihood Ratio Test on Quantiles under a Density Ratio Model

Learning the Basis Function in a Semiparametric Density Ratio Model

Empirical Likelihood Ratio Test on Quantiles under a Density Ratio Model

Density ratio model with data-adaptive basis function

Estimation Efficiency under a Two-Sample Density Ratio Model

teaching

Multiple courses (at both undergraduate and graduate levels) from 2016 to 2021

STAT 302 - Introduction to Probability (Fall 2019, term 2)