TY - JOUR
T1 - On some mixture models based on the Birnbaum-Saunders distribution and associated inference
AU - Balakrishnan, N.
AU - Gupta, Ramesh C.
AU - Kundu, Debasis
AU - Leiva, Víctor
AU - Sanhueza, Antonio
N1 - Funding Information:
The authors wish to thank the anonymous referees for their constructive comments on an earlier version of this manuscript which resulted in this improved version. Research work of V. Leiva and A. Sanhueza was partially supported by FONDECYT 1080326 and 1090265 Grants from Chile.
PY - 2011/7
Y1 - 2011/7
N2 - In this paper, we consider three different mixture models based on the Birnbaum-Saunders (BS) distribution, viz., (1) mixture of two different BS distributions, (2) mixture of a BS distribution and a length-biased version of another BS distribution, and (3) mixture of a BS distribution and its length-biased version. For all these models, we study their characteristics including the shape of their density and hazard rate functions. For the maximum likelihood estimation of the model parameters, we use the EM algorithm. For the purpose of illustration, we analyze two data sets related to enzyme and depressive condition problems. In the case of the enzyme data, it is shown that Model 1 provides the best fit, while for the depressive condition data, it is shown all three models fit well with Model 3 providing the best fit.
AB - In this paper, we consider three different mixture models based on the Birnbaum-Saunders (BS) distribution, viz., (1) mixture of two different BS distributions, (2) mixture of a BS distribution and a length-biased version of another BS distribution, and (3) mixture of a BS distribution and its length-biased version. For all these models, we study their characteristics including the shape of their density and hazard rate functions. For the maximum likelihood estimation of the model parameters, we use the EM algorithm. For the purpose of illustration, we analyze two data sets related to enzyme and depressive condition problems. In the case of the enzyme data, it is shown that Model 1 provides the best fit, while for the depressive condition data, it is shown all three models fit well with Model 3 providing the best fit.
KW - EM algorithm
KW - Fisher information
KW - Goodness-of-fit
KW - Hazard rate function
KW - Inverse Gaussian distribution
KW - Length-biased distributions
KW - Maximum likelihood methods
UR - http://www.scopus.com/inward/record.url?scp=79952248575&partnerID=8YFLogxK
U2 - 10.1016/j.jspi.2010.12.005
DO - 10.1016/j.jspi.2010.12.005
M3 - Article
AN - SCOPUS:79952248575
SN - 0378-3758
VL - 141
SP - 2175
EP - 2190
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
IS - 7
ER -