Likelihood Based Inference and Bias Reduction in the Modified Skew-t-Normal Distribution

Simple item page
datacite.alternateIdentifier.citationMATHEMATICS,Vol.11,2023
datacite.alternateIdentifier.doi10.3390/math11153287
datacite.creatorArrue, Jaime
datacite.creatorArellano-Valle, Reinaldo B.
datacite.creatorCalderin-Ojeda, Enrique
datacite.creatorVenegas, Osvaldo
datacite.creatorGomez, Hector W.
datacite.date2023
datacite.subject.englishmodified skew-t-normal
datacite.subject.englishnon-singular information matrix
datacite.subject.englishmonotone likelihood
datacite.subject.englishmodified likelihood
datacite.subject.englishmodified score
datacite.subject.englishbias prevention
datacite.titleLikelihood Based Inference and Bias Reduction in the Modified Skew-t-Normal Distribution
dc.date.accessioned2024-05-27T18:26:06Z
dc.date.available2024-05-27T18:26:06Z
dc.description.abstractIn this paper, likelihood-based inference and bias correction based on Firth's approach are developed in the modified skew-t-normal (MStN) distribution. The latter model exhibits a greater flexibility than the modified skew-normal (MSN) distribution since it is able to model heavily skewed data and thick tails. In addition, the tails are controlled by the shape parameter and the degrees of freedom. We provide the density of this new distribution and present some of its more important properties including a general expression for the moments. The Fisher's information matrix together with the observed matrix associated with the log-likelihood are also given. Furthermore, the non-singularity of the Fisher's information matrix for the MStN model is demonstrated when the shape parameter is zero. As the MStN model presents an inferential problem in the shape parameter, Firth's method for bias reduction was applied for the scalar case and for the location and scale case.
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/5669
dc.language.isoen
dc.publisherMDPI
dc.sourceMATHEMATICS
oaire.resourceTypeArticle
uct.indizacionSCI
Files
Collections
MigraciĆ³n Web of Science