Work

An iteratively reweighted least squares algorithm for skew-probit binary regression with Firth’s penalization

Year: 2025

Type: article

Abstract: A skew-probit link offers a useful alternative for modeling binary outcomes when symmetric link functions are inadequate due to response asymmetry.This study develops an iteratively reweighted least squares (IRLS) type algorithm tailored for the skew-probit link model, addressing both the naive maximum likelihood estimator (MLE) and Firth's penalized MLE.Through comprehensive numerical studies, the properties of IRLS estimates are investigated and compared with estimates obtained using a numerical optimization approach for both naive and Firth's penalized MLE.Strategies for initial value selection are also suggested to improve algorithmic convergence.We further analyze two real datasets to illustrate the usage of the skew-probit link with the proposed algorithm.

Source: Communications for Statistical Applications and Methods

DOI: 10.29220/csam.2025.32.3.341

Authors: Hojin Yang, DongHyuk Lee

Institution: Pusan National University

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Research Foundation, Pusan National University, National Research Foundation of Korea, Ministry of Science and ICT, South Korea

Awards: G3926182543, G5637276629, G6821800987