Work

Diagnostic analysis in scale mixture of skew‐normal linear mixed models

Year: 2025

Type: article

Abstract: Detecting influential observations and verifying their impact on model fitting and parameter estimation are essential steps in statistical modeling. Different approaches can be utilized to that end, including the case‐deletion method, which evaluates the individual impact on the estimation process, and the local influence approach, which investigates the model sensitivity under some perturbation. This paper introduces case‐deletion measures and influence diagnosis for a flexible class of longitudinal models, the scale mixture of skew‐normal linear mixed models. This approach includes skewed and heavier‐than‐normal‐tailed distributions while accounting for useful within‐subject dependence structures. The method's capability of detecting atypical observations under repeated measurements and the impact of outliers on parameter estimation from models accounting for different distributions are evaluated in simulation studies and a real data illustration.

Source: Statistica Neerlandica

DOI: 10.1111/stan.70002

Authors: Keyliane Travassos, Larissa A. Matos, Fernanda L. Schumacher

Institutions: Universidade Estadual de Campinas (UNICAMP), The Ohio State University

Language: English

Topic: Advanced Statistical Methods and Models

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,800

Funder: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior