Selection of link function in binary regression: A case-study with world happiness report on immigration

TL;DR

This paper introduces a data-driven method for selecting the optimal link function in binary regression, demonstrated through an immigration case-study using the World Happiness Report 2018.

Contribution

It proposes a novel routine for choosing link functions based on classification metrics, enhancing binary regression analysis.

Findings

Effective link function selection improves model inference.

The methodology is demonstrated with real-world happiness and immigration data.

Results show better classification performance with the prescribed routine.

Abstract

Selection of appropriate link function for binary regression remains an important issue for data analysis and its influence on related inference. We prescribe a new data-driven methodology to search for the same, considering some popular classification assessment metrics. A case-study with World Happiness report,2018 with special reference to immigration is presented for demonstrating utility of the prescribed routine.