Laplace Transform driven Stein-type Goodness-of-fit Tests for Pareto Distribution

TL;DR

This paper introduces a new goodness-of-fit test for the Pareto distribution based on Stein's characterization and Laplace transform, with proven asymptotic properties and strong empirical performance.

Contribution

The paper presents a novel Stein-type test for Pareto distribution using Laplace transform, with theoretical validation and superior empirical results.

Findings

The new test often outperforms existing methods in size and power.

The test has well-established asymptotic properties.

Real data applications demonstrate practical utility.

Abstract

The Pareto distribution plays a crucial role in various disciplines, necessitating robust goodness-of-fit tests for its validation. This article introduces a novel tests based on Stein's characterization and the Laplace transform, offering a fresh perspective on model assessment. We establish the asymptotic properties of the proposed test and evaluate its empirical performance against existing methods in terms of size and power. Our findings demonstrate that the new test often outperforms or performs comparably to established tests. In addition, real data applications illustrate its practical utility.