A Class of Bivariate Models with Proportional Reversed Hazard Marginals Debasis Kundu † and Rameshwar D. Gupta ‡ Abstract Recently the proportional reversed hazard model has received a considerable amount of attention in the statistical literature. The main aim of this paper is to introduce a bivariate proportional reversed hazard model and discuss its different properties. In most of the cases the joint probability distribution function can be expressed in com- pact forms. The maximum likelihood estimators cannot be expressed in explicit forms in most of the cases. EM algorithm has been proposed to compute the maximum like- lihood estimators of the unknown parameters. For illustrative purposes two data sets have been analyzed and the performances are quite satisfactory. Keywords: Joint probability density function; Conditional probability density function; Maximum likelihood estimators; EM algorithm. † Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur, Pin 208016, INDIA. Phone no. 91-512-2597141, Fax No. 91-512-2597500, e-mail: kundu@iitk.ac.in. Corresponding author. Part of his work has been supported by a grant from the Department of Science and Technology, Government of India. ‡ Department of Computer Science and Statistics, The University of New Brunswick at Saint John, New Brunswick, Canada E2L 4L5. Part of his work has been supported by a discovery grant from NSERC, CANADA. 1