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ADDITIVE MODELS WITH PREDICTORS SUBJECT TO MEASUREMENT ERROR
Author(s) -
Ganguli Bhaswati,
Staudenmayer John,
Wand M.P.
Publication year - 2005
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2005.00383.x
Subject(s) - mathematics , errors in variables models , monte carlo method , additive model , representation (politics) , maximization , observational error , mixed model , expectation–maximization algorithm , linear model , maximum likelihood , statistics , mathematical optimization , algorithm , politics , political science , law
Summary This paper develops a likelihood‐based method for fitting additive models in the presence of measurement error. It formulates the additive model using the linear mixed model representation of penalized splines. In the presence of a structural measurement error model, the resulting likelihood involves intractable integrals, and a Monte Carlo expectation maximization strategy is developed for obtaining estimates. The method's performance is illustrated with a simulation study.