Inference on P(X less than Y) in bivariate Lomax model


Abstract


In this article we consider the estimation of the stress-strength reliability parameter, R = P(X < Y ) when the stress (X) and the strength (Y ) are dependent random variables distributed as bivariate Lomax model. The maximum likelihood, moment and Bayes estimators are derived. We obtained Bayes estimators using symmetric and asymmetric loss functions via squared error loss and Linex loss functions respectively. Since there are no closed forms for the Bayes estimators, we used an approximation based on Lindley's method to obtain Bayes estimators under these loss functions. An extensive computer simulation is used to compare the performance of the proposed estimators using three criteria, namely, relative bias, mean squared error and Pitman nearness (PN) probability. Real data application is provided to illustrate the performance of our proposed estimators.

DOI Code: 10.1285/i20705948v12n3p619

Keywords: Bivariate Lomax distribution, Lindley's approximation, Pitman nearness probability.

References


Al- Mutaire DK, Ghitany ME and Kundu D (2013). Inference on stress-strength reliability from Lindley distribution, Communications in Statistics-Theory and Methods, 42(8): 1443-1463.

Basu AP and Ebrahimi N (1991). Bayesian approach to life testing and reliability estimation using asymmetric loss function, J Stat Plann Inference, 29: 21-31.

Barbiero A (2012). Interval estimators for reliability: the bivariate normal case, Journal of Applied Statistics, 39: 501-512.

Birnbaum ZW (1956). On a use of Mann-Whitney statistics, Proceedings of the third Berkley Symposium in Mathematics, Statistics and Probability, 1: 13 - 17.

Calabria R and Pulcini G (1989). Confidence limits for reliability and tolerance limits in the inverse Weibull distribution, Reliab Eng Syst Saf, 24:77-85.

Csorgo S and Welsh A.H (1989). Testing for exponential and MarshallOlkin distribution, Journal of Statistical Planning ad Inference, 23:287300.

Domma F and Giordano S (2012). A stress-strength model with dependent variables to measure household financial fragility, Statistical Methods & Applications, 21: 375-389.

Domma F and Giordano S (2013). A copula based approach to account for dependence in stress-strength models, Statistical Papers, 54:807-826.

Gupta RC, Ghitany ME and Al-Mutairi DK (2013). Estimation of reliability from bivariate log normal data, Journal of Statistical Computation and Simulation, 83:1068-1081.

Kotz S, Lumelskii S and Pensky M (2003). The Stress-Strength Model and its Generalizations: Theory and Applications. World Scientic Publishing, Singapore.

Lindley DV (1980). Approximate Bayesian methods, Trabajos de Estadistica, 31: 223-237.

Lindley DV and Singpurwalla ND (1986). Multivariate distributions for the life lengths of a system sharing a common environment, J. Appl. Prob. 23, 481-431.

Makhdoom I, Nasiri P and Pak A (2016). Bayesian approach for the reliability parameter of power Lindley distribution. International Journal of System Assurance Engineering and Management, 3: 341-355.

Nadajarah S (2005). Reliability for some bivariate beta distributions, Mathematical Problems in Engineering, 2005: 101-111.

Rezaei S, Tahmasbi R and Mahmoodi M (2010). Estimation of P[Y < X] for generalized Pareto distribution. Journal of Statistical Planning and Inference, 140: 480-494.

Rubio FJ and Steel MFJ (2013). Bayesian inference of P(X < Y ) using asymmetric dependent distributions, Bayesian Analysis, 8: 43-62.

Samawi HM, Helu A, Rochani HD, Yin J and Linder D (2016). Estimation of P(X < Y ) when X and Y are dependent random variables using dierent bivariate sampling schemes, Communications of Statistical Applications and Methods, 5:385-397.

Soliman A, Abd-Ellah A, Elheggag N and Ahmed E (2012). Modied Weibull model: a Bayes study using MCMC approach based on progressive censoring data, Reliable Eng Syst Saf, 100: 48-57.

Wong (2012). Interval estimation of P(Y < X) for generalized Pareto distribution. Journal of Statistical Planning and Inference, 142: 601-607.

Zellner A (1986). On assessing prior distributions and Bayesian regression analysis with g-prior distributions, In Bayesian Inference and Decision techniques: Essays in Honor of Bruno deFinetti, pp. 233-243.


Full Text: pdf


Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.