Work

Influence diagnostics in the Heckman selection models based on EM algorithms

Year: 2025

Type: article

Abstract: This study presents diagnostic techniques for Heckman selection models estimated using the EM algorithm. The focus is on the selection t and normal models, based on the bivariate Student's-t and bivariate normal distributions, respectively. The Heckman selection model is a key econometric tool for estimating relationships while addressing selection bias. Relying on the EM-type algorithm, we develop global and local influence analyses based on the conditional expectation of the complete-data log-likelihood function, exploring four perturbation schemes for local influence analysis. To assess the effectiveness of the proposed diagnostic measures in identifying influential observations, we conducted a simulation study, complemented by two real-data applications that demonstrate how these techniques can effectively identify influential points. The proposed algorithms and methodologies are incorporated into the R package HeckmanEM.

Source: Journal of Applied Statistics

DOI: 10.1080/02664763.2025.2461715

Authors: Marcos S. Oliveira, Marcos O. Prates, Christian E. Galarza, Victor H. Lachos

Institutions: Federal University of São João del-Rei, Universidade Federal de Minas Gerais, Escuela Superior Politecnica del Litoral, University of Connecticut

Language: English

Topic: Advanced Statistical Methods and Models

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Escuela Superior Politécnica del Litoral, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Awards: INFERÊNCIA BAYESIANA EXATA EM PROCESSOS INFINITO-DIMENSIONAIS, G6317740448, G6367053377