TY - JOUR
T1 - Influence analysis in skew-birnbaum-saunders regression models and applications
AU - Santana, Lucia
AU - Vilca, Filidor
AU - Leiva, Víctor
N1 - Funding Information:
The authors would like to thank the Editor-in-Chief Professor Robert Aykroyd and two anonymous referees for their helpful comments that aided in improving this article. This study was partially supported by a CNPq and FAPESP grant from Brazil and by FONDECYT 1080326 and DIPUV 50-2007 grants from Chile.
PY - 2011/8
Y1 - 2011/8
N2 - 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.
AB - 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.
KW - EM algorithm
KW - Extreme percentiles
KW - Local influence
KW - Sinh-normal distribution
KW - Skew-normal distribution
UR - http://www.scopus.com/inward/record.url?scp=78650848762&partnerID=8YFLogxK
U2 - 10.1080/02664763.2010.515679
DO - 10.1080/02664763.2010.515679
M3 - Article
AN - SCOPUS:78650848762
SN - 0266-4763
VL - 38
SP - 1633
EP - 1649
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 8
ER -