Efficient Robust Estimation for Linear Models with Missing Response at Random.

ABSTRACT: Coefficient estimation in linear regression models with missing data is routinely done in the mean regression framework. However, the mean regression theory breaks down if the error variance is infinite. In addition, correct specification of the likelihood function for existing imputation approach is often challenging in practice, especially for skewed data. In this paper, we develop a novel composite quantile regression and a weighted quantile average estimation procedure for parameter estimation in linear regression models when some responses are missing at random. Instead of imputing the missing response by randomly drawing from its conditional distribution, we propose to impute both missing and observed responses by their estimated conditional quantiles given the observed data and to u

SUBMITTER: Tang ML 

PROVIDER: S-EPMC6070309 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature