The bivariate generalized linear failure rate distribution and its multivariate extension

The two-parameter linear failure rate distribution has been used quite successfully to analyze lifetime data. Recently, a new three-parameter distribution, known as the generalized linear failure rate distribution, has been introduced by exponentiating the linear failure rate distribution. The generalized linear failure rate distribution is a very flexible lifetime distribution, and the probability density function of the generalized linear failure rate distribution can take different shapes. Its hazard function also can be increasing, decreasing and bathtub shaped. The main aim of this paper is to introduce a bivariate generalized linear failure rate distribution, whose marginals are generalized linear failure rate distributions. It is obtained using the same approach as was adopted to obtain the Marshall-Olkin bivariate exponential distribution. Different properties of this new distribution are established. The bivariate generalized linear failure rate distribution has five parameters and the maximum likelihood estimators are obtained using the EM algorithm. A data set is analyzed for illustrative purposes. Finally, some generalizations to the multivariate case are proposed.

[1]  Magne Vollan Aarset,et al.  How to Identify a Bathtub Hazard Rate , 1987, IEEE Transactions on Reliability.

[2]  Simos G. Meintanis Test of fit for Marshall–Olkin distributions with applications , 2007 .

[3]  Debasis Kundu,et al.  Bivariate generalized exponential distribution , 2009, J. Multivar. Anal..

[4]  Deo Kumar Srivastava,et al.  The exponentiated Weibull family: a reanalysis of the bus-motor-failure data , 1995 .

[5]  Alok Pandey,et al.  Bayes estimation of the linear hazard-rate model , 1993 .

[6]  Manuel Franco,et al.  A multivariate extension of Sarhan and Balakrishnan's bivariate distribution and its ageing and dependence properties , 2010, J. Multivar. Anal..

[7]  Bill Ravens,et al.  An Introduction to Copulas , 2000, Technometrics.

[8]  Narayanaswamy Balakrishnan,et al.  A new class of bivariate distributions and its mixture , 2007 .

[9]  E. Lehmann Some Concepts of Dependence , 1966 .

[10]  Narayanaswamy Balakrishnan,et al.  Monte Carlo Methods for Bayesian Inference on the Linear Hazard Rate Distribution , 2006 .

[11]  Filippo Domma,et al.  Some properties of the bivariate Burr type III distribution , 2010 .

[12]  Lee J. Bain,et al.  Interval Estimation for the Two-parameter Double Exponential Distribution , 1973 .

[13]  H. Joe Multivariate models and dependence concepts , 1998 .

[14]  Narayanaswamy Balakrishnan,et al.  Parameter Estimation for the Linear Hazard Rate Distribution Based on Records and Inter-record Times , 2003 .

[15]  Richard E. Barlow,et al.  Statistical Theory of Reliability and Life Testing: Probability Models , 1976 .

[16]  H. Akaike Fitting autoregressive models for prediction , 1969 .

[17]  Nils Blomqvist,et al.  On a Measure of Dependence Between two Random Variables , 1950 .

[18]  Debasis Kundu,et al.  Generalized Linear Failure Rate Distribution , 2009 .

[19]  I. Olkin,et al.  A Multivariate Exponential Distribution , 1967 .

[20]  Gunky Kim,et al.  Comparison of semiparametric and parametric methods for estimating copulas , 2007, Comput. Stat. Data Anal..

[21]  G. K. Bhattacharyya,et al.  Inference procedures for the linear failure rate model , 1995 .

[22]  G. Schwarz Estimating the Dimension of a Model , 1978 .