Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the expectation-maximization algorithm give consistent estimators for model parameters when data are missing at random (MAR) provided that the response model and the missing covariate model are correctly specified; however, we do not need to specify the missing data mechanism. An alternat...

Covariate shift is a fundamental problem for learning in non-stationary environments where the conditional distribution ppy|xq is the same between training and test data while their marginal distributions ptrpxq and ptepxq are different. Although many covariate shift correction techniques remain effective for real world problems, most do not scale well in practice. In this paper, using inspirat...

This paper considers identification and estimation of a nonparametric regression model with an unobserved discrete covariate. The sample consists of a dependent variable and a set of covariates, one of which is discrete and arbitrarily correlates with the unobserved covariate. The observed discrete covariate has the same support as the unobserved covariate, and can be interpreted as a proxy or ...

Proportional hazard models for survival data, even though popular and numerically handy, suffer from the restrictive assumption that covariate effects are constant over survival time. A number of tests have been proposed to check this assumption. This paper contributes to this area by employing local estimates allowing to fit hazard models in which covariate effects are smoothly varying with ti...

Shifts in the marginal distribution of covariates from training to test phase, named covariate-shifts, often lead unstable prediction performance across agnostic testing data, especially under model misspecification. Recent literature on invariant learning attempts learn an predictor heterogeneous environments. However, learned depends heavily availability and quality provided In this paper, we...