Analytical Quantile Solution for the S-distribution, Random Number Generation and Statistical Data Modeling

TL;DR

This paper introduces an analytical quantile solution for the S-distribution, enhancing its application in data modeling, random number generation, and fitting procedures, thereby offering a flexible alternative to classical distributions.

Contribution

It provides the first analytical solution to the quantile equation of the S-distribution, simplifying its use in modeling, sampling, and fitting to data.

Findings

Analytical quantile solution simplifies S-distribution applications.

Enables generation of distributions with specific probability constraints.

Facilitates fitting S-distributions to empirical data.

Abstract

The selection of a specific statistical distribution is seldom a simple problem. One strategy consists in testing different distributions (normal, lognormal, Weibull, etc.), and selecting the one providing the best fit to the observed data and being the most parsimonious. Alternatively, one can make a choice based on theoretical arguments and simply fit the corresponding parameters to the observed data. In either case, different distributions can give similar results and provide almost equivalent results. Model selection can be more complicated when the goal is to describe a trend in the distribution of a given variable. In those cases, changes in shape and skewness are difficult to represent by a single distributional form. As an alternative to the use of complicated families of distributions as models for data, the S-distribution [{\sc Voit, E.O. }(1992) Biom.J. 7:855-878] provides a highly flexible mathematical form in which the density is defined as a function of the cumulative. Besides representing well-known distributions, S-distributions provide an infinity of new possibilities that do not correspond with known classical distributions. In this paper we obtain an analytical solution for the quantile equation that highly simplifies the use of S-distributions. We show the utility of this solution in different applications. After classifying the different qualitative behaviors of the S-distribution in parameter space, we show how to obtain different S-distributions that accomplish specific constraints. One interesting case is the possibility of obtaining distributions that acomplish P(X <= X_c)=0. Then, we show that the quantile solution facilitates the use of S-distributions in Monte-Carlo experiments through the generation of random samples. Finally, we show how to fit an S-distribution to actual data, so that the resulting distribution can be used as a statistical model for them.