The transfer principle: A tool for complete case analysis

TL;DR

This paper introduces a transfer principle for deriving the asymptotic distributions of complete case statistics in missing data models, simplifying the analysis of inference procedures without extensive proofs.

Contribution

It presents a general method to obtain limiting distributions of complete case statistics from full data results, facilitating efficient inference in missing data scenarios.

Findings

Complete case estimators of the slope are shown to be efficient.

An asymptotically distribution free test for normality is developed.

A new test for linearity in partially linear models is proposed.

Abstract

This paper gives a general method for deriving limiting distributions of complete case statistics for missing data models from corresponding results for the model where all data are observed. This provides a convenient tool for obtaining the asymptotic behavior of complete case versions of established full data methods without lengthy proofs. The methodology is illustrated by analyzing three inference procedures for partially linear regression models with responses missing at random. We first show that complete case versions of asymptotically efficient estimators of the slope parameter for the full model are efficient, thereby solving the problem of constructing efficient estimators of the slope parameter for this model. Second, we derive an asymptotically distribution free test for fitting a normal distribution to the errors. Finally, we obtain an asymptotically distribution free test for linearity, that is, for testing that the nonparametric component of these models is a constant. This test is new both when data are fully observed and when data are missing at random.