Stochastic Processes with Modified Lognormal Distribution Featuring Flexible Upper Tail

TL;DR

This paper introduces a flexible family of modified lognormal distributions based on kappa-functions, enabling better modeling of skewed data with heavy or light tails, and applies these to stochastic processes and time series analysis.

Contribution

It develops a new three-parameter distribution family with analytic properties, parameter estimation methods, and applications to stochastic processes and forecasting.

Findings

Kappa-lognormal densities have lighter or bimodal tails.

Closed-form expressions for statistical functions are derived.

Applications to time series forecasting and spatial interpolation are demonstrated.

Abstract

Asymmetric, non-Gaussian probability distributions are often observed in the analysis of natural and engineering datasets. The lognormal distribution is a standard model for data with skewed frequency histograms and fat tails. However, the lognormal law severely restricts the asymptotic dependence of the probability density and the hazard function for high values. Herein we present a family of three-parameter non-Gaussian probability density functions that are based on generalized kappa-exponential and kappa-logarithm functions and investigate its mathematical properties. These kappa-lognormal densities represent continuous deformations of the lognormal with lighter right tails, controlled by the parameter kappa. In addition, bimodal distributions are obtained for certain parameter combinations. We derive closed-form analytic expressions for the main statistical functions of the kappa-lognormal distribution. For the moments, we derive bounds that are based on hypergeometric functions as well as series expansions. Explicit expressions for the gradient and Hessian of the negative log-likelihood are obtained to facilitate numerical maximum-likelihood estimates of the kappa-lognormal parameters from data. We also formulate a joint probability density function for kappa-lognormal stochastic processes by applying Jacobi's multivariate theorem to a latent Gaussian process. Estimation of the kappa-lognormal distribution based on synthetic and real data is explored. Furthermore, we investigate applications of kappa-lognormal processes with different covariance kernels in time series forecasting and spatial interpolation using warped Gaussian process regression. Our results are of practical interest for modeling skewed distributions in various scientific and engineering fields.