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Exact Inference for Growth Curves with Intraclass Correlation Structure *
Author(s) -
Weerahandi Samaradasa,
Berger Vance W.
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00921.x
Subject(s) - intraclass correlation , inference , mathematics , growth curve (statistics) , confidence interval , statistics , exact statistics , statistical inference , null hypothesis , simple (philosophy) , computer science , artificial intelligence , philosophy , epistemology , psychometrics
Summary. We consider repeated observations taken over time for each of several subjects. For example, one might consider the growth curve of a cohort of babies over time. We assume a simple linear growth curve model. Exact results based on sufficient statistics (exact tests of the null hypothesis that a coefficient is zero, or exact confidence intervals for coefficients) are not available to make inference on regression coefficients when an intraclass correlation structure is assumed. This paper will demonstrate that such exact inference is possible using generalized inference.