Skew-normal distribution for growth curve models in presence of a heteroscedasticity structure

In general, growth models are adjusted under the assumptions that the error terms are homoscedastic and normally distributed. However, these assumptions are often not verified in practice. In this work we propose four growth models (Morgan-Mercer-Flodin, von Bertalanffy, Gompertz, and Richards) cons...

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Bibliographic Details
Published in:Journal of applied statistics 2014-08, Vol.41 (8), p.1785-1798
Main Authors: Louzada, Francisco, Ferreira, Paulo H., Diniz, Carlos A.R.
Format: Article
Language:English
Subjects:
Adjustment
Applied statistics
Computer simulation
Covariance
Errors
Estimating techniques
Growth models
homoscedasticity with autocorrelation lag 1
Maximum likelihood estimation
Maximum likelihood method
Methodology
multiplicative heteroscedasticity
Normal distribution
Simulation
simulation study
skew-normal distribution
Studies
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Summary:In general, growth models are adjusted under the assumptions that the error terms are homoscedastic and normally distributed. However, these assumptions are often not verified in practice. In this work we propose four growth models (Morgan-Mercer-Flodin, von Bertalanffy, Gompertz, and Richards) considering different distributions (normal, skew-normal) for the error terms and three different covariance structures. Maximum likelihood estimation procedure is addressed. A simulation study is performed in order to verify the appropriateness of the proposed growth curve models. The methodology is also illustrated on a real dataset.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2014.891005