Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data

Abstract

A residual-based empirical distribution function is proposed to estimate the distribution function of the errors of a heteroskedastic nonparametric regression with responses missing at random based on completely observed data, and this estimator is shown to be asymptotically most precise.

Preprint is 20 pages in length

References in corpus (2)

• The transfer principle: A tool for complete case analysis
• Efficiently estimating the error distribution in nonparametric regression with responses missing at random