A new imputation method for incomplete binary data

被引：8

作者：

Subasi, Munevver Mine ^{[2
]}

Subasi, Ersoy ^{[1
]}

Anthony, Martin ^{[3
]}

Hammer, Peter L.

机构：

[1] Rutgers Ctr Operat Res, RUTCOR, Piscataway, NJ 08854 USA

[2] Florida Inst Technol, Dept Math Sci, Melbourne, FL 32901 USA

[3] Univ London London Sch Econ & Polit Sci, Dept Math, London WC2A 2AE, England

来源：

DISCRETE APPLIED MATHEMATICS | 2011年 / 159卷 / 10期

关键词：

Imputation; Boolean similarity measure; MISSING DATA MECHANISM; LOGICAL ANALYSIS; LIKELIHOOD; REGRESSION; MODELS;

D O I：

10.1016/j.dam.2011.01.024

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In data analysis problems where the data are represented by vectors of real numbers, it is often the case that some of the data-points will have "missing values", meaning that one or more of the entries of the vector that describes the data-point is not observed. In this paper, we propose a new approach to the imputation of missing binary values. The technique we introduce employs a "similarity measure" introduced by Anthony and Hammer (2006) [1]. We compare experimentally the performance of our technique with ones based on the usual Hamming distance measure and multiple imputation. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：1040 / 1047

页数：8

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