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Hypothesis testing in an errors‐in‐variables model with heteroscedastic measurement errors
Author(s) -
de Castro Mário,
Galea Manuel,
Bolfarine Heleno
Publication year - 2008
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.3343
Subject(s) - heteroscedasticity , statistics , estimator , econometrics , bivariate analysis , inference , observational error , variance (accounting) , goodness of fit , computer science , errors in variables models , maximum likelihood , mathematics , accounting , artificial intelligence , business
In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error‐prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model's goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright © 2008 John Wiley & Sons, Ltd.