A Multi-Aspect Permutation Test for Goodness-of-Fit Problems

Rosa Arboretti, Elena Barzizza, Nicolò Biasetton, Riccardo Ceccato, Livio Corain and Luigi Salmaso
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Rosa Arboretti: Department of Civil, Environmental and Architectural Engineering, University of Padova, 35131 Padova, Italy
Elena Barzizza: Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy
Nicolò Biasetton: Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy
Riccardo Ceccato: Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy
Livio Corain: Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy
Luigi Salmaso: Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy

Stats, 2022, vol. 5, issue 2, 1-11

Abstract: Parametric techniques commonly rely on specific distributional assumptions. It is therefore fundamental to preliminarily identify the eventual violations of such assumptions. Therefore, appropriate testing procedures are required for this purpose to deal with a the goodness-of-fit (GoF) problem. This task can be quite challenging, especially with small sample sizes and multivariate data. Previous studiesshowed how a GoF problem can be easily represented through a traditional two-sample system of hypotheses. Following this idea, in this paper, we propose a multi-aspect permutation-based test to deal with the multivariate goodness-of-fit, taking advantage of the nonparametric combination (NPC) methodology. A simulation study is then conducted to evaluate the performance of our proposal and to identify the eventual critical scenarios. Finally, a real data application is considered.

Keywords: multi-aspect; NPC; goodness-of-fit (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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