Empirical Survival Jensen-Shannon Divergence as a Goodness-of-Fit Measure for Maximum Likelihood Estimation and Curve Fitting

TL;DR

This paper introduces the Survival Jensen-Shannon divergence as a nonparametric goodness-of-fit measure for maximum likelihood estimation and curve fitting, validated through simulations and real data.

Contribution

It proposes a novel divergence measure, the ${ m E}SJS$, for assessing fit quality in parametric distribution fitting, extending the Jensen-Shannon divergence.

Findings

${ m E}SJS$ effectively measures goodness-of-fit.

The method constructs confidence intervals for fitted models.

Validated with simulated and empirical datasets.

Abstract

The coefficient of determination, known as $R^2$, is commonly used as a goodness-of-fit criterion for fitting linear models. $R^2$ is somewhat controversial when fitting nonlinear models, although it may be generalised on a case-by-case basis to deal with specific models such as the logistic model. Assume we are fitting a parametric distribution to a data set using, say, the maximum likelihood estimation method. A general approach to measure the goodness-of-fit of the fitted parameters, which is advocated herein, is to use a nonparametric measure for comparison between the empirical distribution, comprising the raw data, and the fitted model. In particular, for this purpose we put forward the Survival Jensen-Shannon divergence ($SJS$) and its empirical counterpart (${\cal E}SJS$) as a metric which is bounded, and is a natural generalisation of the Jensen-Shannon divergence. We demonstrate, via a straightforward procedure making use of the ${\cal E}SJS$, that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution. Furthermore, we show the validity of the proposed method with simulated data, and three empirical data sets.