TY - JOUR
T1 - Random number generators for the generalized Birnbaum-Saunders distribution
AU - Leiva, Víctor
AU - Sanhueza, Antonio
AU - Sen, Pranab K.
AU - Paula, Gilberto A.
N1 - Funding Information:
The authors wish to thank the AE and referees for their helpful comments that greatly improved this article. This study was supported by Grants FONDECYT 1050862, FANDES C-13955(10), DIPUV 42-2004, and DIUFRO 120321, Chile, CAPES, CNPq, and Fapesp, Brazil. This article was written during the time that Dr. Leiva was a Visiting Scholar at the University of North Carolina (UNC) at Chapel Hill, USA. He is especially grateful to the Collaborate Studies Coordinating Center (CSCC) of the Department of Biostatistics, UNC at Chapel Hill, USA, for its support.
PY - 2008
Y1 - 2008
N2 - The generalized Birnbaum-Saunders distribution pertains to a class of lifetime models including both lighter and heavier tailed distributions. This model adapts well to lifetime data, even when outliers exist, and has other good theoretical properties and application perspectives. However, statistical inference tools may not exist in closed form for this model. Hence, simulation and numerical studies are needed, which require a random number generator. Three different ways to generate observations from this model are considered here. These generators are compared by utilizing a goodness-of-fit procedure as well as their effectiveness in predicting the true parameter values by using Monte Carlo simulations. This goodness-of-fit procedure may also be used as an estimation method. The quality of this estimation method is studied here. Finally, through a real data set, the generalized and classical Birnbaum-Saunders models are compared by using this estimation method.
AB - The generalized Birnbaum-Saunders distribution pertains to a class of lifetime models including both lighter and heavier tailed distributions. This model adapts well to lifetime data, even when outliers exist, and has other good theoretical properties and application perspectives. However, statistical inference tools may not exist in closed form for this model. Hence, simulation and numerical studies are needed, which require a random number generator. Three different ways to generate observations from this model are considered here. These generators are compared by utilizing a goodness-of-fit procedure as well as their effectiveness in predicting the true parameter values by using Monte Carlo simulations. This goodness-of-fit procedure may also be used as an estimation method. The quality of this estimation method is studied here. Finally, through a real data set, the generalized and classical Birnbaum-Saunders models are compared by using this estimation method.
KW - Elliptical distributions
KW - Goodness-of-fit
KW - Inverse Gaussian distribution
KW - Monte Carlo simulation
KW - Sinh-normal distribution
UR - http://www.scopus.com/inward/record.url?scp=38649086796&partnerID=8YFLogxK
U2 - 10.1080/00949650701550242
DO - 10.1080/00949650701550242
M3 - Article
AN - SCOPUS:38649086796
SN - 0094-9655
VL - 78
SP - 1105
EP - 1118
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
IS - 11
ER -