On classification with nonignorable missing data
type: Item
instance of: scholarly article
title: On classification with nonignorable missing data
zbMATH Open document ID: 1473.62225
author: Majid Mojirsheibani
published in: Journal of Multivariate Analysis
publication date: 2021-06-22T00:00:00Z
review text: \textit{J. K. Kim} and \textit{C. L. Yu} [J. Am. Stat. Assoc. 106, No. 493, 157--165 (2011; Zbl 1396.62032)] proposed an efficient tool to estimate unknown population parameters based on nonignorable missing data with a semiparametric exponential tilting propensity. Such missing response mechanism was employed in this paper to construct new kernel classification rules that can be used to perform statistical classification with nonignorable missing responses. The strong asymptotic optimality of the proposed kernel classifiers is established.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62D10
zbMATH DE Number: 7361738
zbMATH Keywords: classification
zbMATH Keywords: convergence
zbMATH Keywords: kernel
zbMATH Keywords: missing data
zbMATH Keywords: regression
reviewed by: Denis Sidorov
MaRDI profile type: Publication
full work available at URL: j.jmva.2021.104755
OpenAlex ID: W3138067111
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DOI: 10.1016/J.JMVA.2021.104755
description: scientific article; zbMATH DE number 7361738