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A Laplace-based model with flexible tail behavior

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dc.contributor.authorTortora, Cristina
dc.contributor.authorFranczak, Brian C.
dc.contributor.authorBagnato, Luca
dc.contributor.authorPunzo, Antonio
dc.date.accessioned2025-01-31T16:52:50Z
dc.date.available2025-01-31T16:52:50Z
dc.date.issued2024
dc.description.abstractThe proposed multiple scaled contaminated asymmetric Laplace (MSCAL) distribution is an extension of the multivariate asymmetric Laplace distribution to allow for a different excess kurtosis on each dimension and for more flexible shapes of the hyper-contours. These peculiarities are obtained by working on the principal component (PC) space. The structure of the MSCAL distribution has the further advantage of allowing for automatic PC-wise outlier detection – i.e., detection of outliers separately on each PC – when convenient constraints on the parameters are imposed. The MSCAL is fitted using a Monte Carlo expectation-maximization (MCEM) algorithm that uses a Monte Carlo method to estimate the orthogonal matrix of eigenvectors. A simulation study is used to assess the proposed MCEM in terms of computational efficiency and parameter recovery. In a real data application, the MSCAL is fitted to a real data set containing the anthropometric measurements of monozygotic/dizygotic twins. Both a skewed bivariate subset of the full data, perturbed by some outlying points, and the full data are considered.
dc.identifier.citationTortora C., Franczak B. C., Bagnato, L., & Punzo A. (2024) A Laplace-based model with flexible tail behavior. Computational Statistics and Data Analysis, 192, 107909. https://doi.org/10.1016/j.csda.2023.107909
dc.identifier.doihttps://doi.org/10.1016/j.csda.2023.107909
dc.identifier.urihttps://hdl.handle.net/20.500.14078/3773
dc.language.isoen
dc.rightsAttribution-NonCommercial-NoDerivs (CC BY-NC-ND)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectcontaminated distributions
dc.subjectdirectional outlier detection
dc.subjectMonte Carlo expectation-maximization algorithm
dc.subjectmultiple scaled distributions
dc.subjectnormal variance-mean mixtures
dc.titleA Laplace-based model with flexible tail behavioren
dc.typeArticle

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