A New Class of Generalized Logarithmic Series Distribution with Non-Negative Support and its Applications
Keywords:
Count data models, maximum likelihood estimation, Markov chain-Monte Carlo simulation, probability generating function, zero-inflated distributionAbstract
The study of the logarithmic series distribution (LSD) with non-negative support received much attention in the literature due to its practical relevance. Through this paper, we propose a new class of LSD with non-negative support as a modified form of the generalized LSD of Wimmer and Altmann (Sankhya, 1995) and study some of its important aspects. The parameters of this class of LSD are estimated by various methods of estimation and illustrated the procedures with the help of certain real life data applications. Further, a brief simulation study is attempted for assessing the performance of the estimators.
