Inferences on a normal covariance matrix and generalized variance with monotone missing data

DOI10.1006/jmva.2000.1939zbMath0978.62050OpenAlexW1980449741MaRDI QIDQ5943594

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Publication date: 4 February 2002

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.2000.1939

zbMATH Keywords

likelihood ratio testmissing datageneralized variancemonotone patterns powerSatterthwaite approximation

Mathematics Subject Classification ID

Multivariate analysis (62H99) Parametric tolerance and confidence regions (62F25) Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15)

Related Items (26)

Interval estimation of the common mean and difference of medians for a bivariate lognormal distribution ⋮ Finite-sample inference with monotone incomplete multivariate normal data. I. ⋮ Finite-sample inference with monotone incomplete multivariate normal data. II ⋮ The Stein phenomenon for monotone incomplete multivariate normal data ⋮ Discrimination of observations into one of two elliptic populations based on monotone training samples ⋮ Two-sample inference for normal mean vectors based on monotone missing data ⋮ Testing equality of two normal covariance matrices with monotone missing data ⋮ Effect of nonnormality on tests for a mean vector with missing data under an elliptically contoured pattern-mixture model ⋮ Bartlett correction to the likelihood ratio test for MCAR with two‐step monotone sample ⋮ Simultaneous tests for mean vectors and covariance matrices with three-step monotone missing data ⋮ Equivalence testing of mean vector and covariance matrix for multi-populations under a two-step monotone incomplete sample ⋮ Kurtosis tests for multivariate normality with monotone incomplete data ⋮ Test for high-dimensional mean vector under missing observations ⋮ Estimation of Poisson-Dirichlet parameters with 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 ⋮ Asymptotic expansion for distribution of the trace of a covariance matrix under a 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 normality test based on kurtosis with two-step monotone missing data ⋮ On the likelihood ratio test for the equality of multivariate normal populations with two-step monotone missing data ⋮ Multivariate regression with consecutively added dependent variables ⋮ On the Gaussian representation of the Riesz probability distribution on symmetric matrices ⋮ An exact test for equality of two normal mean vectors with monotone missing data ⋮ On the Exact and Near-Exact Distributions of the Product of Generalized Gamma Random Variables and the Generalized Variance

Cites Work

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• Some simple test procedures for normal mean vector with incomplete data
• Improved Minimax Estimators of Normal Covariance and Precision Matrices from Incomplete Samples
• Maximum likelihood estimation for multivariate normal distribution with monotone sample
• Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing
• On testing equality of means of correlated variables with incomplete data
• Inference and missing data
• Confidence estimation of a normal mean vector with incomplete data
• Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
• Properties of Power Functions of Some Tests Concerning Dispersion Matrices of Multivariate Normal Distributions
• A test for equality of means of correlated variates with missing data on one response
• A Significance Test for Component Analysis
• Estimation of Parameters from Incomplete Data

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