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Optimal estimation in surrogate outcome regression problems
Author(s) -
Duan Xiaogang,
Qin Jing,
Wang Qihua
Publication year - 2010
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.10086
Subject(s) - outcome (game theory) , estimator , statistics , regression analysis , regression , propensity score matching , mathematics , covariance matrix , econometrics , computer science , mathematical economics
The authors consider a double robust estimation of the regression parameter defined by an estimating equation in a surrogate outcome set‐up. Under a correct specification of the propensity score, the proposed estimator has smallest trace of asymptotic covariance matrix whether the “working outcome regression model” involved is specified correct or not, and it is particularly meaningful when it is incorrectly specified. Simulations are conducted to examine the finite sample performance of the proposed procedure. Data on obesity and high blood pressure are analyzed for illustration. The Canadian Journal of Statistics 38: 633–646; 2010 © 2010 Statistical Society of Canada