Diagnostics in multivariate generalized Birnbaum-Saunders regression models. Issue 15 (17th November 2016)

• Record Type: Journal Article
• Authors: Marchant, Carolina; Leiva, Víctor; Cysneiros, Francisco José A.; Vivanco, Juan F.
• Is Part Of: Journal of applied statistics. Volume 43:Issue 15(2016)
• Page Start: 2829
• Page End: 2849
• Publication Date: 2016-11-17
• Subjects: Birnbaum–Saunders distributions -- global and local influence -- goodness-of-fit -- multivariate data analysis -- R software; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1148671
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Birnbaum–Saunders (BS) models are receiving considerable attention in the literature. Multivariate regression models are a useful tool of the multivariate analysis, which takes into account the correlation between variables. Diagnostic analysis is an important aspect to be considered in the statistical modeling. In this paper, we formulate multivariate generalized BS regression models and carry out a diagnostic analysis for these models. We consider the Mahalanobis distance as a global influence measure to detect multivariate outliers and use it for evaluating the adequacy of the distributional assumption. We also consider the local influence approach and study how a perturbation may impact on the estimation of model parameters. We implement the obtained results in theR software, which are illustrated with real-world multivariate data to show their potential applications.