Uni- and multivariate extensions of the sinh-arcsinh normal distribution applied to distributional regression
Yuanhua Feng () and
Wolfgang Härdle ()
No 142, Working Papers CIE from Paderborn University, CIE Center for International Economics
This paper introduces first an extended SAN (sinh-arcsinh normal) family of distri- butions by allowing the transformed normal random variable to be unstandardized. A Log-SAN transformation for non-negative random variables and the associate Log-SAN family of distributions are then proposed. Properties of those distribu- tions are investigated. A maximum likelihood estimation procedure is proposed. A chain mixed multivariate extension of the SAN distributions and a corresponding distributional regression model are then defined. Those approaches can help us to discover possible spurious or hidden bimodal property of a multivariate distribution. The proposals are illustrated by different examples.
Keywords: Extended SAS distribution; Log-SAS distribution; MLE; chain mixed multivariate distribution; distributional regression; spurious and hidden bimodality (search for similar items in EconPapers)
JEL-codes: C14 C51 (search for similar items in EconPapers)
Pages: 32 pages
New Economics Papers: this item is included in nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pdn:ciepap:142
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