On parameter estimation for the truncated skew-normal distribution

TL;DR

This paper introduces a grid-based estimation method called GRID-MOM for the truncated skew-normal distribution, improving stability and accuracy in parameter estimation by decoupling shape from other parameters.

Contribution

The paper proposes a novel grid-based estimation approach that enhances numerical stability and accuracy for the truncated skew-normal distribution.

Findings

The method provides stable and accurate estimates across various scenarios.

It outperforms existing methods in finite-sample performance.

Practical applications demonstrate its usefulness in real data analysis.

Abstract

Parameter estimation for the truncated skew-normal distribution is challenging, as truncation introduces additional nonlinearity into the likelihood function and often leads to numerical instability in existing estimation procedures. In this paper, we propose a grid-based estimation method, referred to as GRID-MOM, for parameter estimation in the truncated skew-normal distribution. The proposed approach fixes the shape parameter on a pre-specified grid and, for each grid point, estimates the location and scale parameters using the method of moments. The optimal value of the shape parameter is then selected via likelihood-based comparison, yielding the final parameter estimates. By decoupling the estimation of the shape parameter from that of the location and scale parameters, the proposed method reduces the complexity of the optimization problem and improves numerical stability. We evaluate the finite-sample performance of the proposed estimator through an extensive numerical study, comparing it with existing methods under a variety of scenarios. The results demonstrate that the proposed method provides stable and accurate estimation, particularly for the shape parameter, suggesting that the proposed method offers a practical alternative for inference in truncated skew-normal models. We further demonstrate the practical applicability of the proposed method using phosphoproteomics data and hospital admission data.