TY - JOUR
T1 - Birnbaum–Saunders distribution based on asymmetric heavy-tailed distributions, associated inference, and application
AU - Maehara, Rocío
AU - Bolfarine, Heleno
AU - Vilca, Filidor
AU - Balakrishnan, N.
N1 - Publisher Copyright:
© Allerton Press, Inc. 2025.
PY - 2025/3
Y1 - 2025/3
N2 - Birnbaum–Saunders (BS) distribution has received considerable attention in the statistical literature, both in applied and theoretical problems. Even though much work has been done on extensions of the BS distribution, there is still a need for models for predicting extreme percentiles and for fitting data that are highly concentrated on the left-tail of the distribution. This article proposes a robust extension of the BS distribution, based on scale mixtures of skew-normal distributions that can be used to model highly asymmetric data. This extension provides flexible heavy-tailed distributions which can be used in the robust estimation of parameters in the presence of outlying observations, as well as an EM-algorithm for the maximum likelihood estimation of model parameters. Finally, the proposed model and methods of inference are examined and illustrated by means of Monte Carlo simulation studies and a real data set.
AB - Birnbaum–Saunders (BS) distribution has received considerable attention in the statistical literature, both in applied and theoretical problems. Even though much work has been done on extensions of the BS distribution, there is still a need for models for predicting extreme percentiles and for fitting data that are highly concentrated on the left-tail of the distribution. This article proposes a robust extension of the BS distribution, based on scale mixtures of skew-normal distributions that can be used to model highly asymmetric data. This extension provides flexible heavy-tailed distributions which can be used in the robust estimation of parameters in the presence of outlying observations, as well as an EM-algorithm for the maximum likelihood estimation of model parameters. Finally, the proposed model and methods of inference are examined and illustrated by means of Monte Carlo simulation studies and a real data set.
KW - Probabilidad aplicada
KW - Estadística aplicada
KW - Teoría de distribuciones
KW - Estadística matemática
KW - Modelado estocástico en estadística
KW - Teoría y métodos estadísticos
KW - Applied probability
KW - Applied statistics
KW - Distribution theory
KW - Mathematical statistics
KW - Stochastic modelling in statistics
KW - Statistical theory and methods
KW - Birnbaum–Saunders distribution
KW - scale mixtures of skew-normal distributions
KW - skew-normal distribution
KW - EM algorithm
UR - https://www.scopus.com/pages/publications/105007473647
UR - https://www.mendeley.com/catalogue/d764739e-acf4-336c-81f2-e6bc5530ef1a/
U2 - 10.3103/S1066530723600355
DO - 10.3103/S1066530723600355
M3 - Article in a journal
SN - 1066-5307
VL - 34
SP - 34
EP - 53
JO - Mathematical Methods of Statistics
JF - Mathematical Methods of Statistics
IS - 1
ER -