{"title":"On the Mai–Wang stochastic decomposition for ℓp-norm symmetric survival functions on the positive orthant","authors":"Christian Genest , Johanna G. Nešlehová","doi":"10.1016/j.jmva.2024.105331","DOIUrl":null,"url":null,"abstract":"<div><p>Recently, Mai and Wang (2021) investigated a class of <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>-norm symmetric survival functions on the positive orthant. In their paper, they claim that the generator of these functions must be <span><math><mi>d</mi></math></span>-monotone. This note explains that this is not true in general. Luckily, most of the results in Mai and Wang (2021) are not affected by this oversight.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0047259X24000381/pdfft?md5=f0a3613b1587ac23eed097d6f63a0a06&pid=1-s2.0-S0047259X24000381-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Multivariate Analysis","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0047259X24000381","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
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Abstract
Recently, Mai and Wang (2021) investigated a class of -norm symmetric survival functions on the positive orthant. In their paper, they claim that the generator of these functions must be -monotone. This note explains that this is not true in general. Luckily, most of the results in Mai and Wang (2021) are not affected by this oversight.
期刊介绍：
Founded in 1971, the Journal of Multivariate Analysis (JMVA) is the central venue for the publication of new, relevant methodology and particularly innovative applications pertaining to the analysis and interpretation of multidimensional data.
The journal welcomes contributions to all aspects of multivariate data analysis and modeling, including cluster analysis, discriminant analysis, factor analysis, and multidimensional continuous or discrete distribution theory. Topics of current interest include, but are not limited to, inferential aspects of
Copula modeling
Functional data analysis
Graphical modeling
High-dimensional data analysis
Image analysis
Multivariate extreme-value theory
Sparse modeling
Spatial statistics.
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