Influence analysis in skew-birnbaum-saunders regression models and applications

Lucia Santana, Filidor Vilca, Víctor Leiva

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

In this paper, we propose a method to assess influence in skew-Birnbaum-Saunders regression models, which are an extension based on the skew-normal distribution of the usual Birnbaum-Saunders (BS) regression model. An interesting characteristic that the new regression model has is the capacity of predicting extreme percentiles, which is not possible with the BS model. In addition, since the observed likelihood function associated with the new regression model is more complex than that from the usual model, we facilitate the parameter estimation using a type-EM algorithm. Moreover, we employ influence diagnostic tools that considers this algorithm. Finally, a numerical illustration includes a brief simulation study and an analysis of real data in order to show the proposed methodology.

Original languageEnglish
Pages (from-to)1633-1649
Number of pages17
JournalJournal of Applied Statistics
Volume38
Issue number8
DOIs
StatePublished - Aug 2011
Externally publishedYes

Keywords

  • EM algorithm
  • Extreme percentiles
  • Local influence
  • Sinh-normal distribution
  • Skew-normal distribution

Fingerprint

Dive into the research topics of 'Influence analysis in skew-birnbaum-saunders regression models and applications'. Together they form a unique fingerprint.

Cite this