An Absolute Continuous Bivariate Inverse Generalized Exponential Distribution: Properties, Inference and Extensions Debasis Kundu ∗ Abstract The aim of this paper is to introduce an absolutely continuous bivariate inverse generalized exponential (BIGE) distribution. The proposed distribution has been ob- tained by removing the singular component from the BIGE distribution similarly as the Block and Basu absolute continuous bivariate exponential distribution. This dis- tribution has four parameters, and due to this the joint probability density function can take variety of shapes. This distribution can be used quite effectively if there are no ties in the bivariate data set and particularly if the marginals are from a heavy tailed distribution. We have developed different properties of this distribution and provide classical inference of the unknown parameters. The maximum likelihood (ML) estimators cannot be obtained in closed form and one needs to solve a four dimensional optimization problem to compute ML estimators in this case. To avoid that we propose to use expectation maximization (EM) algorithm to compute the ML estimators of the unknown parameters. The analysis of one data set has been performed to see the effec- tiveness of the proposed algorithm and we extend the results to the multivariate case also. Finally we conclude the paper with several open problems for future research. Key Words and Phrases: Marshall-Olkin bivariate exponential distribution; Block and Basu bivariate distributions;maximum likelihood estimators; EM algorithm; competing risks. AMS Subject Classifications: 62F10, 62F03, 62H12. * Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Pin 208016, India. E-mail: kundu@iitk.ac.in, Phone no. 91-512-2597141, Fax no. 91-512-2597500. 1