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

Abstract

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.

Keywords:

• growth models
• homoscedasticity with autocorrelation lag 1
• multiplicative heteroscedasticity
• skew-normal distribution
• maximum likelihood estimation
• simulation study