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Bayesian Methods for Regression Using Surrogate Variables
Author(s) -
Manner David,
Seaman John W.,
Young Dean M.
Publication year - 2004
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200210073
Subject(s) - frequentist inference , regression analysis , statistics , bayesian probability , regression , variable (mathematics) , regression diagnostic , surrogate model , bayesian linear regression , econometrics , variables , linear regression , mathematics , computer science , bayesian inference , bayesian multivariate linear regression , mathematical analysis
If a dependent variable in a regression analysis is exceptionally expensive or hard to obtain the overall sample size used to fit the model may be limited. To avoid this one may use a cheaper or more easily collected “surrogate” variable to supplement the expensive variable. The regression analysis will be enhanced to the degree the surrogate is associated with the costly dependent variable. We develop a Bayesian approach incorporating surrogate variables in regression based on a two‐stage experiment. Illustrative examples are given, along with comparisons to an existing frequentist method. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)