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A Property of the CHAID Partitioning Method for Dichotomous Randomized Response Data and Categorical Predictors

A Property of the CHAID Partitioning Method for Dichotomous Randomized Response Data and... In this paper, we present empirical and theoretical results on classification trees for randomized response data. We considered a dichotomous sensitive response variable with the true status intentionally misclassified by the respondents using rules prescribed by a randomized response method. We assumed that classification trees are grown using the Pearson chi-square test as a splitting criterion, and that the randomized response data are analyzed using classification trees as if they were not perturbed. We proved that classification trees analyzing observed randomized response data and estimated true data have a one-to-one correspondence in terms of ranking the splitting variables. This is illustrated using two real data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

A Property of the CHAID Partitioning Method for Dichotomous Randomized Response Data and Categorical Predictors

Journal of Classification , Volume 29 (1) – Oct 29, 2011

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References (17)

Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media, LLC
Subject
Statistics; Statistical Theory and Methods; Signal, Image and Speech Processing; Pattern Recognition; Psychometrics; Bioinformatics; Marketing
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-011-9094-8
Publisher site
See Article on Publisher Site

Abstract

In this paper, we present empirical and theoretical results on classification trees for randomized response data. We considered a dichotomous sensitive response variable with the true status intentionally misclassified by the respondents using rules prescribed by a randomized response method. We assumed that classification trees are grown using the Pearson chi-square test as a splitting criterion, and that the randomized response data are analyzed using classification trees as if they were not perturbed. We proved that classification trees analyzing observed randomized response data and estimated true data have a one-to-one correspondence in terms of ranking the splitting variables. This is illustrated using two real data sets.

Journal

Journal of ClassificationSpringer Journals

Published: Oct 29, 2011

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