In this paper, we propose a new class of asymptotically e¢ cient estimators for moment condition models. These estimators share the same higher order bias properties as the generalized empirical likelihood estimators and once bias corrected, have the same higher order e¢ ciency properties as the bias corrected generalized empirical likelihood estimators. Unlike the generalized empirical likelih...

The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Wit...

We propose a varying-coefficient quantile regression model for survival data subject to random censoring. Motivated by the work of Yang (1999), quantilebased moments are constructed using covariate-weighted empirical cumulative hazard functions. We estimate regression parameters based on the generalized method of moments. The proposed estimators are shown to be consistent and asymptotically nor...

We devise a one-shot approach to distributed sparse regression in the high-dimensional setting. The key idea is to average " debiased " or " desparsified " lasso estimators. We show the approach converges at the same rate as the lasso as long as the dataset is not split across too many machines. We also extend the approach to generalized linear models.

This article presents a new method for maximum likelihood estimation of logistic regression models with incomplete covariate data where auxiliary information is available. This auxiliary information is extraneous to the regression model of interest but predictive of the covariate with missing data. Ibrahim (1990, Journal of the American Statistical Association 85, 765-769) provides a general me...

There is interest in extending the boosting algorithm (Schapire, 1990) to fit a wide range of regression problems. The threshold-based boosting algorithm for regression used an analogy between classification errors and big errors in regression. We focus on the practical aspects of this algorithm and compare it to other attempts to extend boosting to regression. The practical capabilities of thi...

In this paper, we consider the estimation of both the parameters and the nonparametric link function in partially linear single-index models for longitudinal data which may be unbalanced. In particular, a new three-stage approach is proposed to estimate the nonparametric link function using marginal kernel regression and the parametric components with generalized estimating equations. The resul...