Computation of Two and Three Dimensional Confidence Regions with the Likelihood Ratio

TL;DR

This paper introduces a simple trigonometric method to numerically compute two and three dimensional likelihood ratio-based confidence regions, enabling visual representation of parameter plausibility in various distributions.

Contribution

It presents a novel, distribution-agnostic approach using radial profile log likelihood for graphical confidence region computation in multiple dimensions.

Findings

Method is applicable to any distribution with a likelihood function.

The approach enables efficient visualization of confidence regions.

Computation time is analyzed and found to be practical.

Abstract

The asymptotic results pertaining to the distribution of the log likelihood ratio allow for the creation of a confidence region, which is a general extension of the confidence interval. Two and three dimensional regions can be displayed visually in order to describe the plausible region of the parameters of interest simultaneously. While most advanced statistical textbooks on inference discuss these asymptotic confidence regions, there is no exploration of how to numerically compute these regions for graphical purposes. This article demonstrates the application of a simple trigonometric identity to compute two and three dimensional confidence regions, we transform the Cartesian coordinates to create what we call the radial profile log likelihood. The method is applicable to any distribution with a defined likelihood function, so it is not limited to specific data distributions or model paradigms. We describe the method along with the algorithm, follow with an example of our method and end with an examination of computation time.