Log-concavity and other concepts of bivariate increasing failure rate distributions
Clayton's measure of association, hazard gradient, Hessian matrix, local dependence function, log-concave density, Schur-concavity
Journal of Applied Probability
Log-concavity of a joint survival function is proposed as a model for bivariate increasing failure rate (BIFR) distributions. Its connections with or distinctness from other notions of BIFR are discussed. A necessary and sufficient condition for a bivariate survival function to be log-concave (BIFR-LCC) is given that elucidates the impact of dependence between lifetimes on ageing. Illustrative examples are provided to explain BIFR-LCC for both positive and negative dependence.
Department of Mathematics
Original Publication Date
DOI of published version
Gupta, Ramesh C. and Kirmani, S. N.U.A., "Log-concavity and other concepts of bivariate increasing failure rate distributions" (2022). Faculty Publications. 5216.