In this paper, we study the bivariate log-normal distribution from a reliability point of view. The conditional distribution of X given Y > y is found to be log-skew normal. The monotonicity of the hazard rates of the univariate as well as the conditional distributions is discussed. Clayton's association measure is obtained in terms of the hazard gradient and its value in the case of our model is derived. The probability distributions, in the case of series and parallel systems, are derived and the monotonicity of their failure rates is discussed. Three real applications of the bivariate log-normal distribution are provided, two from financial economics and one from reliability.
Keywords: Hazard components; monotonicity; association measure; series and parallel systems
Biography: Professor Pushpa Gupta has been a faculty member at the University of Maine for over 30 years. Her research interests include Distribution theory, Inflated models, Reliability and survival analysis.