Estimation and Influence Diagnostics for the Multivariate Linear Regression Models with Skew Scale Mixtures of Normal Distributions

Graciliano M. S. Louredo, Camila B. Zeller and Clécio S. Ferreira ()
Additional contact information
Graciliano M. S. Louredo: Universidade Federal de Juiz de Fora
Camila B. Zeller: Universidade Federal de Juiz de Fora
Clécio S. Ferreira: Universidade Federal de Juiz de Fora

Sankhya B: The Indian Journal of Statistics, 2022, vol. 84, issue 1, No 8, 204-242

Abstract: Abstract In this paper, we present recent results in the context of multivariate linear regression models considering that random errors follow multivariate skew scale mixtures of normal distributions. This class of distributions includes the scale mixtures of multivariate normal distributions, as special cases, and provides flexibility in capturing a wide variety of asymmetric behaviors. We implemented the algorithm ECM (Expectation/Conditional Maximization) and we obtained closed-form expressions for all the estimators of the parameters of the proposed model. Inspired by the ECM algorithm, we have developed an influence diagnostics for detecting influential observations to investigate the sensitivity of the maximum likelihood estimators. To examine the performance and the usefulness of the proposed methodology, we present simulation studies and analyze a real dataset.

Keywords: Skew scale mixtures of normal distributions; multivariate linear regression; ECM algorithm; global and local influence.; Primary; 62J05, 62F10, Secondary; 62J20, 62F12 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s13571-021-00257-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sankhb:v:84:y:2022:i:1:d:10.1007_s13571-021-00257-y

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13571

DOI: 10.1007/s13571-021-00257-y

Access Statistics for this article

Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().