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A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models

Author

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  • Zahra Barkhordar
  • Mohsen Maleki
  • Zahra Khodadadi
  • Darren Wraith
  • Farajollah Negahdari

Abstract

In this application note paper, we propose and examine the performance of a Bayesian approach for a homoscedastic nonlinear regression (NLR) model assuming errors with two-piece scale mixtures of normal (TP-SMN) distributions. The TP-SMN is a large family of distributions, covering both symmetrical/ asymmetrical distributions as well as light/heavy tailed distributions, and provides an alternative to another well-known family of distributions, called scale mixtures of skew-normal distributions. The proposed family and Bayesian approach provides considerable flexibility and advantages for NLR modelling in different practical settings. We examine the performance of the approach using simulated and real data.

Suggested Citation

  • Zahra Barkhordar & Mohsen Maleki & Zahra Khodadadi & Darren Wraith & Farajollah Negahdari, 2022. "A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(5), pages 1305-1322, April.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1305-1322
    DOI: 10.1080/02664763.2020.1854203
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