Manuscript Topics

Statistical distributions are used to model the data in many research domains, such as medicine, engineering, finance, and humanities.  One of the most important aspects of statistical distributions is their characterization. These characteristics are calculated depending on the researcher's goal, including specifying probability density function and distribution function, moments and moment generating function, hazard rate function, quantile function, mean residual lifetime, and so on.

The process of data modeling using statistical distributions is the most important stage of statistical analysis. Choosing an inappropriate statistical distribution for data modeling will lead to inaccurate results impacting decision-making. In recent years, researchers' interest has increased in studying a new type of distribution called "compound distribution" to represent the data that cannot be represented by well-recognized statistical distributions. Compound distributions denote the distributions that result from combining two or more distributions.

This special issue aims to publish state-of-the-art research papers on data modeling using compound, generalized, extended, family type, and mixed distributions in theory and application aspects. We highly encourage and invite researchers to contribute to this special issue by submitting high-quality research and review articles in these areas.

Keywords: Compound Distributions  Generalized Distributions  Extended Distributions  Family Type Distributions  Statistical Modeling  Estimation  Hazard Function  Mean Residual Lifetime  Moments  Reliability

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