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

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  • Francisco Louzada
  • Paulo H. Ferreira
  • Carlos A.R. Diniz

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.

Suggested Citation

  • Francisco Louzada & Paulo H. Ferreira & Carlos A.R. Diniz, 2014. "Skew-normal distribution for growth curve models in presence of a heteroscedasticity structure," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1785-1798, August.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1785-1798
    DOI: 10.1080/02664763.2014.891005
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    Cited by:

    1. Karim, Md Aktar Ul & Bhagat, Supriya Ramdas & Bhowmick, Amiya Ranjan, 2022. "Empirical detection of parameter variation in growth curve models using interval specific estimators," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).

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