Generalized q-logistic distribution

Abstract

Several generalizations of the logistic distribution and certain related models are proposed by many authors for modeling various random phenomena such as those encountered in data engineering, pattern recognition, and reliability assessment studies. In this paper, we study generalized q-logistic (GqL) distribution, in which the additional parameters offered increase flexibility of the distribution for modeling purposes. Since the parameter estimation of GqL model is not explored yet, in the present study, we propose different methods for estimating the GqL parameters. In particular, we have made a comprehensive comparison through simulation study, the performance of the maximum likelihood estimation (both complete and censored), maximum product spacing estimation and ordinary least squares estimation methods. Some characterization theorems are obtained and some properties of GqL are established. Finally, we analyze a real data to illustrate the potentiality of the model and also present some graphical illustrations.