Parametric and Nonparametric Regression with Missing X’s—A Review

Bayesian Nonparametric and Parametric Inference

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations.

We consider nonparametric regression of a scalar outcome on a covariate when the outcome is missing at random (MAR) given the covariate and other observed auxiliary variables. We propose a class of augmented inverse probability weighted (AIPW) kernel estimating equations for nonparametric regression under MAR. We show that AIPW kernel estimators are consistent when the probability that the outc...

Nonparametric Regression with a Parametric Spatial Autoregressive Error Structure

Spatial models have received considerable attention in the last decade. At the same time, researchers have also started to embrace the flexibility afforded from nonparametric methods. However, methods that allow for nonparametric aspects of models whose errors exhibit spatial dependence have only recently been explored. We propose a fully nonparametric estimator of the regression function allow...

Nonparametric regression estimation with general parametric error covariance

Recently Martins-Filho and Yao (J Multivar Anal 100:309–333, 2009) have proposed a two-step estimator of nonparametric regression function with parametric error covariance and demonstrate that it is more efficient than the usual LLE. In the present paper we demonstrate that MY’s estimator can be further improved. First, we extend MY’s estimator to the multivariate case, and also establish the a...

Nonparametric Regression

This article has no abstract.

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates