Abstract
Employing simulated data, several methods for estimating correlation and variance-covariance matrices are studied for observations missing at random from data matrices. The effect of sample size, number of variables, percent of missing data and average intercorrelations of variables are examined for several proposed methods.
Keywords
CovarianceStatisticsMissing dataMathematicsCovariance matrixCorrelationCovariance and correlationAnalysis of covarianceVariance (accounting)Sample mean and sample covarianceSample size determinationCovariance mappingEstimation of covariance matricesOne-way analysis of varianceAnalysis of varianceEstimationCovariance intersectionEconometricsRandom variableSum of normally distributed random variablesIndependent and identically distributed random variablesEstimator
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Publication Info
- Year
- 1970
- Type
- article
- Volume
- 35
- Issue
- 4
- Pages
- 417-437
- Citations
- 80
- Access
- Closed
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Cite This
Neil H. Timm
(1970).
The Estimation of Variance-Covariance and Correlation Matrices from Incomplete Data.
Psychometrika
, 35
(4)
, 417-437.
https://doi.org/10.1007/bf02291818
Identifiers
- DOI
- 10.1007/bf02291818