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Testing for Random Effects in Growth Curve Models

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  • Zaixing Li

Abstract

Growth curve models (GCMs) are useful and Demidenko (2004) considered the presence of random effects under the normal assumptions about random effects and random errors. It is also of interest to remove distribution assumptions to investigate the same problem. A difference-based test is constructed for GCMs, which can be regarded as an extension of Li and Zhu (2010)’s method and a complement to Demidenko (2004) where his test is exact in small samples. Without any distribution assumptions, our test derived for GCMs is asymptotically a standard normal. The power properties are also investigated. Besides, simulations are carried out to examine its performance.

Suggested Citation

  • Zaixing Li, 2015. "Testing for Random Effects in Growth Curve Models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(3), pages 564-572, February.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:3:p:564-572
    DOI: 10.1080/03610926.2012.746988
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    Cited by:

    1. Shinpei Imori & Dietrich Rosen & Ryoya Oda, 2022. "Growth Curve Model with Bilinear Random Coefficients," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 477-508, August.

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