Allometric analysis using the multivariate shifted exponential normal distribution

DOI10.1002/BIMJ.201900248zbMath1448.62184OpenAlexW3014487718WikidataQ91510752 ScholiaQ91510752MaRDI QIDQ5135552

Antonio Punzo, Luca Bagnato

Publication date: 23 November 2020

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201900248

zbMATH Keywords

exponential distributionheavy-tailed distributionsmaximum likelihoodelliptical distributionsallometryscale mixtures

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (9)

Two new matrix-variate distributions with application in model-based clustering ⋮ An overview of heavy-tail extensions of multivariate Gaussian distribution and their relations ⋮ New bivariate and multivariate log-normal distributions as models for insurance data ⋮ Dimension-wise scaled normal mixtures with application to finance and biometry ⋮ Model-based clustering via new parsimonious mixtures of heavy-tailed distributions ⋮ Parsimonious mixtures for the analysis of tensor-variate data ⋮ High-dimensional hypothesis testing for allometric extension model ⋮ Multivariate Contaminated Normal Censored Regression Model: Properties and Maximum Likelihood Inference ⋮ Unconstrained representation of orthogonal matrices with application to common principal components

This page was built for publication: Allometric analysis using the multivariate shifted exponential normal distribution