A note on the maximum likelihood estimators for multivariate normal distribution with monotone data

DOI10.1080/03610929508831560zbMath0850.62420OpenAlexW2022314381MaRDI QIDQ4883454

Hironori Fujisawa

Publication date: 2 July 1996

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929508831560

zbMATH Keywords

maximum likelihood estimatormissing dataconditional distributionmultivariate normal distributionmonotone data

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12)

Related Items (11)

Finite-sample inference with monotone incomplete multivariate normal data. I. ⋮ Finite-sample inference with monotone incomplete multivariate normal data. II ⋮ The maximum likelihood estimators in a multivariate normal distribution with \(AR(1)\) covariance structure for monotone data ⋮ EPEM: efficient parameter estimation for multiple class monotone missing data ⋮ Errors in discrimination with monotone missing data from multivariate normal populations ⋮ Unbiased Estimator for a Covariance Matrix Under Two-Step Monotone Incomplete Sample ⋮ Statistical inference for location and scale of elliptically contoured models with monotone missing data ⋮ Unbiased estimator for a covariance matrix in a three-step monotone incomplete sample ⋮ Multivariate Linear Regression Model with Elliptically Contoured Distributed Errors and Monotone Missing Dependent Variables ⋮ Multivariate regression with consecutively added dependent variables ⋮ Some Basic Properties of the Mle's for a Multivariate Normal Distribution with Monotone Missing Data

Cites Work

• Unnamed Item
• Maximum-likelihood estimation of the parameters of a multivariate normal distribution
• Some one-sample hypothesis testing problems when there is a monotone sample from a multivariate normal population
• Maximum likelihood estimation for multivariate normal distribution with monotone sample
• Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing

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