Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

Victor H. Lachos, Dipankar Bandyopadhyay, Aldo M. Garay

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. We derive a simple EM-type algorithm for iteratively computing maximum likelihood (ML) estimates and the observed information matrix is derived analytically. Simulation studies demonstrate the robustness of this flexible class against outlying and influential observations, as well as nice asymptotic properties of the proposed EM-type ML estimates. Finally, the methodology is illustrated using an ultrasonic calibration data.

Original languageEnglish (US)
Pages (from-to)1208-1217
Number of pages10
JournalStatistics and Probability Letters
Volume81
Issue number8
DOIs
StatePublished - Aug 1 2011

Keywords

  • EM algorithm
  • Homogeneity
  • Nonlinear regression models
  • Scale mixtures
  • Skew-normal

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