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Average Collapsibility of Distribution Dependence and Quantile Regression Coefficients

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Title Average Collapsibility of Distribution Dependence and Quantile Regression Coefficients
 
Creator VELLAISAMY, P
 
Subject average collapsibility
collapsibility
conditional independence
contingency table
distribution dependence
quantile regression coefficient
Yule-Simpson paradox
MULTIDIMENSIONAL CONTINGENCY-TABLES
ASSOCIATION
 
Description . The YuleSimpson paradox notes that an association between random variables can be reversed when averaged over a background variable. Cox and Wermuth introduced the concept of distribution dependence between two random variables X and Y, and gave two dependence conditions, each of which guarantees that reversal of qualitatively similar conditional dependences cannot occur after marginalizing over the background variable. Ma, Xie and Geng studied the uniform collapsibility of distribution dependence over a background variable W, under stronger homogeneity condition. Collapsibility ensures that associations are the same for conditional and marginal models. In this article, we use the notion of average collapsibility, which requires only the conditional effects average over the background variable to the corresponding marginal effect and investigate its conditions for distribution dependence and for quantile regression coefficients.
 
Publisher WILEY-BLACKWELL
 
Date 2014-10-14T13:08:29Z
2014-10-14T13:08:29Z
2012
 
Type Article
 
Identifier SCANDINAVIAN JOURNAL OF STATISTICS, 39(1)153-165
http://dx.doi.org/10.1111/j.1467-9469.2011.00757.x
http://dspace.library.iitb.ac.in/jspui/handle/100/14482
 
Language en