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Longšriš®š«: a test for bump hunting in longitudinal data
Author(s) -
Harezlak Jaroslaw,
Naumova Elena,
Laird Nan M.
Publication year - 2006
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.2623
Subject(s) - statistics , mathematics , longitudinal data , monotone polygon , population , maxima and minima , spline (mechanical) , test (biology) , econometrics , computer science , demography , mathematical analysis , data mining , paleontology , geometry , structural engineering , sociology , biology , engineering
We propose an extension of the Harezlak and Heckman ( J. Comput. Graph. Statist. 2001; 10 (4): 713ā729) test for detecting local extrema to the longitudinal data setting. We use penalized spline regression techniques ( Statist. Sci. 1996; 11 :89ā102) to provide a computationally efficient method of testing for relatively large data sets. We estimate the p āvalues of our test, Longri, with a smoothed bootstrap. Our simulation studies indicate that the test is generally conservative and has power exceeding 70 per cent at the Ī±=0.1 nominal level in most considered settings. Finally, we apply our testing procedure to the longitudinal measurements of body mass index of former prisoners of war in Vietnam and conclude that the mean population curve exhibits nonāmonotone behaviour. Copyright Ā© 2006 John Wiley & Sons, Ltd.