Missing data in the k-opulation multivariate normal patteknei) mean and covariance matrix testing and estimation problem

T. H. Szatrowski

doi:10.1080/03610918508812444

Missing data in the k-opulation multivariate normal patteknei) mean and covariance matrix testing and estimation problem

T. H. Szatrowski

Rutgers Business School, Management & Information Systems

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Maximum likelihood estimates and likelihood ratio statistics and their asymptotic null and nonnull distributions are derived for the k-population testing and estimation problem with patterned means and covariance matrices in the presence of missing data. These results are an extension of results of Szatrowski (1979) on the k-population complete data problem and Szatrowski (1983) on the one-population missing data problem. The standard delta method is the principal technique used for deriving the asymptotic nonnull distributions.

Original language	English (US)
Pages (from-to)	357-370
Number of pages	14
Journal	Communications in Statistics - Simulation and Computation
Volume	14
Issue number	2
DOIs	https://doi.org/10.1080/03610918508812444
State	Published - Jan 1 1985

All Science Journal Classification (ASJC) codes

Statistics and Probability
Modeling and Simulation

Keywords

Asymptotic efficiency
asymptotic nonnull
delta method
distribution
hypothesis testing
iterative computations
matrix derivatives
maximum likelihood estimates
missing data

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10.1080/03610918508812444

Cite this

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title = "Missing data in the k-opulation multivariate normal patteknei) mean and covariance matrix testing and estimation problem",

abstract = "Maximum likelihood estimates and likelihood ratio statistics and their asymptotic null and nonnull distributions are derived for the k-population testing and estimation problem with patterned means and covariance matrices in the presence of missing data. These results are an extension of results of Szatrowski (1979) on the k-population complete data problem and Szatrowski (1983) on the one-population missing data problem. The standard delta method is the principal technique used for deriving the asymptotic nonnull distributions.",

keywords = "Asymptotic efficiency, asymptotic nonnull, delta method, distribution, hypothesis testing, iterative computations, matrix derivatives, maximum likelihood estimates, missing data",

author = "Szatrowski, {T. H.}",

note = "Funding Information: This research was partially supported by National Science Foundation Grants MCS77-28184 and MCS83-01718, the Program Statistics Research Project of the Educational Testing Service Research Statistics Group, and Rutgers University Graduate School of Management Summer Research Grants.",

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T1 - Missing data in the k-opulation multivariate normal patteknei) mean and covariance matrix testing and estimation problem

AU - Szatrowski, T. H.

N1 - Funding Information: This research was partially supported by National Science Foundation Grants MCS77-28184 and MCS83-01718, the Program Statistics Research Project of the Educational Testing Service Research Statistics Group, and Rutgers University Graduate School of Management Summer Research Grants.

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Y1 - 1985/1/1

N2 - Maximum likelihood estimates and likelihood ratio statistics and their asymptotic null and nonnull distributions are derived for the k-population testing and estimation problem with patterned means and covariance matrices in the presence of missing data. These results are an extension of results of Szatrowski (1979) on the k-population complete data problem and Szatrowski (1983) on the one-population missing data problem. The standard delta method is the principal technique used for deriving the asymptotic nonnull distributions.

AB - Maximum likelihood estimates and likelihood ratio statistics and their asymptotic null and nonnull distributions are derived for the k-population testing and estimation problem with patterned means and covariance matrices in the presence of missing data. These results are an extension of results of Szatrowski (1979) on the k-population complete data problem and Szatrowski (1983) on the one-population missing data problem. The standard delta method is the principal technique used for deriving the asymptotic nonnull distributions.

KW - Asymptotic efficiency

KW - asymptotic nonnull

KW - delta method

KW - distribution

KW - hypothesis testing

KW - iterative computations

KW - matrix derivatives

KW - maximum likelihood estimates

KW - missing data

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JO - Communications in Statistics - Simulation and Computation

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IS - 2

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Missing data in the k-opulation multivariate normal patteknei) mean and covariance matrix testing and estimation problem

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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