TY - JOUR
T1 - Extreme value Birnbaum–Saunders regression models applied to environmental data
AU - Leiva, Víctor
AU - Ferreira, Marta
AU - Gomes, M. Ivette
AU - Lillo, Camilo
N1 - Funding Information:
The authors wish to thank the Editors and three anonymous referees for their constructive comments on an earlier version of this manuscript, which resulted in this improved version. This study was partially supported by the Chilean Council for Scientific and Technological Research under the project grant FONDECYT 1120879, by FEDER Funds through “Programa Operacional de Factores de Competitividade-COMPETE” and by Portuguese Funds through “Fundação para a Ciência e a Tecnologia” (FCT) under the project Grants PEst-OE/MAT/UI0006/2014 (CEAUL) and PEst-OE/MAT/UI0013/2014.
Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.
PY - 2016/3/1
Y1 - 2016/3/1
N2 - Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.
AB - Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.
KW - Data analysis
KW - Maximum likelihood method
KW - Monte Carlo simulation
KW - Residuals
KW - Statistical modeling
UR - http://www.scopus.com/inward/record.url?scp=84959254914&partnerID=8YFLogxK
U2 - 10.1007/s00477-015-1069-6
DO - 10.1007/s00477-015-1069-6
M3 - Article
AN - SCOPUS:84959254914
VL - 30
SP - 1045
EP - 1058
JO - Stochastic Environmental Research and Risk Assessment
JF - Stochastic Environmental Research and Risk Assessment
SN - 1436-3240
IS - 3
ER -