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An improved test of latent‐variable model misspecification in structural measurement error models for group testing data
Author(s) -
Huang Xianzheng
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3698
Subject(s) - latent variable , inference , computer science , latent variable model , structural equation modeling , variable (mathematics) , data mining , specification , errors in variables models , statistics , econometrics , machine learning , artificial intelligence , mathematics , mathematical analysis
We consider structural measurement error models for group testing data. Likelihood inference based on structural measurement error models requires one to specify a model for the latent true predictors. Inappropriate specification of this model can lead to erroneous inference. We propose a new method tailored to detect latent‐variable model misspecification in structural measurement error models for group testing data. Compared with the existing diagnostic methods developed for the same purpose, our method shows vast improvement in the power to detect latent‐variable model misspecification in group testing design. We illustrate the implementation and performance of the proposed method via simulation and application to a real data example. Copyright © 2009 John Wiley & Sons, Ltd.