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Assessment of variance components in nonlinear mixed-effects elliptical models

Content Provider	Springer Nature Link
Author	Russo, Cibele M. Aoki, Reiko Paula, Gilberto A.
Copyright Year	2011
Abstract	The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.
Starting Page	519
Ending Page	545
Page Count	27
File Format	PDF
ISSN	11330686
Journal	Test
Volume Number	21
Issue Number	3
e-ISSN	18638260
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-07-30
Publisher Institution	Spanish Society of Statistics and Operations Research
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Nonlinear models Elliptical distributions Hypothesis testing Variance components Score tests Robustness and adaptive procedures Statistics for Business/Economics/Mathematical Finance/Insurance Statistical Theory and Methods Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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