Variable Selection for the Growth Curve Model
AbstractIn this paper we consider the problem of selecting the covariables within individuals in the growth curve model. We propose two modifications ofAICandMIC(Cp-static), which have improvements on the bias properties. Asymptotic distributions of variable slection criteria are derived under a general situation where a polynomial growth curve of degreej0is approximately suitable. A simulation study is also given to gain some understanding on the small sample properties of these variable selection criteria
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 60 (1997)
Issue (Month): 2 (February)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Hu, Jianhua & Xin, Xin & You, Jinhong, 2014. "Model determination and estimation for the growth curve model via group SCAD penalty," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 199-213.
- Siotani, Minoru & Wakaki, Hirofumi, 2006. "Contributions to multivariate analysis by Professor Yasunori Fujikoshi," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 1914-1926, October.
- Fujikoshi, Yasunori & Enomoto, Rie & Sakurai, Tetsuro, 2013. "High-dimensional AIC in the growth curve model," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 239-250.
- Soler, Julia M. P. & Singer, Julio M., 2000. "Optimal covariance adjustment in growth curve models," Computational Statistics & Data Analysis, Elsevier, vol. 33(1), pages 101-110, March.
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