Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations

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...

Estimating Missing Values Using Mixture Kernel Regression

One of the important problem in data quality is the presence of missing data. So missing data imputation is an important issue in learning from incomplete data. Imputation is a procedure that replaces the missing values in a data set by some plausible values. Various techniques have been developed to deal with missing values in data sets with homogenous attributes. But those approaches are inde...

Estimating Rate Equations Using Nonparametric Regression Methods

Weighted Kernel Estimators in Nonparametric Binomial Regression

This paper is concerned with nonparametric binomial regression. Two kernel-based binomial regression estimators and their bias-adjusted versions are proposed, whose kernels are weighted by the inverses of variance estimators of the observed proportion at each covariate. Asymptotic theories for deriving asymptotic mean squared errors (AMSEs) of proposed estimators are developed. Comparisons with...

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

This paper gives a detailed overview of the problem of missing data in parametric and nonparametric regression. Theoretical basics, properties as well as simulation results may help the reader to get familiar with the common problem of incomplete data sets. Of course, not all occurences can be discussed so this paper could be seen as an introduction to missing data within regression analysis an...