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The estimation of variancecovariance and correlation matrices from incomplete data
 Neil H. Timm
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Employing simulated data, several methods for estimating correlation and variancecovariance 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.
The author is indebted to Professors Leonard A. Marascuilo, Gus W. Haggstrom, especially Henry F. Kaiser for their invaluable suggestions throughout this work. Appreciation is also extended to the Computer Center facility of the University of California at Berkeley for the use of computer time to complete the necessary computations.
 Afifi, A. & Elashoff, R. M. “Missing observations in multivariate statistics I. Review of the literature,”Journal of the American Statistical Association,61, 1966, pp. 595–604.
 Afifi, A. & Elashoff, R. M. “Missing observations in multivariate statistics II. Point estimation in simple linear regression,”Journal of the American Statistical Association,62, 1967, pp. 10–29.
 Anderson, T. W. “Maximum likelihood estimates for a multivariate normal distribution when some observations are missing,”Journal of the American Statistical Association,52, 1957, pp. 200–203.
 Buck, S. F. “A method of estimation of missing values in multivariate data suitable for use with an electronic computer,”Journal of the Royal Statistical Society, Series B,22, 1960, pp. 302–307.
 Christoffersson, A. “A method for component analysis when the data are incomplete,” Seminar Communication, University Institute of Statistics, Uppsala, 1965.
 Coleman, J. S.Equality of Education Opportunity, United States Government Printing Office, Washington,11, 1966.
 Dear, R. E. “A principlecomponent missing data method for multiple regression models,” System Development Corporation, Technical report SP86, 1959.
 Dempster, A. P.Elements of Continuous Multivariate Analysis, Addison and Wesley, Massachusetts, 1969.
 Edgett, G. L. “Multiple regression with missing observations among the independent variables,”Journal of the American Statistical Association,51, 1956, pp. 122–131.
 Federspiel, C. F. Monroe, R. J., & Greenberg, B. G., “An investigation of some multiple regression methods for incomplete samples,” University of North Carolina, Institute of Statistics, Mineo Series, No. 236, August 1959.
 Glasser, M. “Linear regression analysis with missing observations among the independent variables,”Journal of the American Statistical Association,59 1964, pp. 834–844.
 Haitovsky, Y. “Missing data in regression analysis,”Journal of the Royal Statistical Society, Series B,30, 1968, pp. 67–82.
 Hocking, R. R. & Smith, W. B. “Estimation of parameters in the multivariate normal distribution with missing observations,”Journal of the American Statistical Association,63, 1968, pp. 159–173.
 Jackson, E. C. “Missing values in linear multiple discriminant analysis,”Biometrics,24 1968, pp. 835–844.
 Jensen, A. 1969, Personal commumication.
 Kaiser, H. F. “A measure of the average intercorrelation,”Educational and Psychological Measurement,28, 1968, pp. 245–247.
 Kaiser, H. F. & Dickman, K. “Sample and population score matrices and sample correlation matrices from an arbitrary population correlation matrix,”Psychometrika,27, 1962, pp. 179–182.
 Kendall, M. G.A Course in Multivariate Analysis, Hafner, New York, 1961.
 Lawley, D. N., & Maxwell, A. E.Factor Analysis as a Statistical Method, Butterworths, London, 1963.
 Lord, Frederick M. “Estimation of parameters from incomplete data,”Journal of the American Statistical Association,50, 1955a, pp. 870–876.
 Lord, Frederick M. “Equating test scores—a maximum likelihood solution,”Psychometrika,20, 1955b, pp. 193–200.
 Matthai, A. “Estimation of parameters from incomplete data with application to design of sample surveys,”Sankhya,2, 1951, pp. 145–152.
 Nicholson, G. E., Jr. “Estimation of parameters from incomplete multivariate samples,”Journal of the American Statistical Association,52, 1957, pp. 523–536.
 Rao, C. R. “Analysis of dispersion with incomplete observations on one of the characters,”Journal of the Royal Statistical Society, Series B,18, 1956, pp. 259–264.
 Timm, Neil H. “Estimating variancecovariance and correlation matrices from incomplete data,” (unpublished Doctoral Dissertation), Berkeley, California.
 Trawinski, I. M. & Bargmann, R., “Maximum likelihood estimation with incomplete multivariate data,”Annals of Mathematical Statistics,35, 1964, pp. 647–657.
 Tukey, J. W. “Bias and confidence in not quite large samples,”Annals of Mathematical Statistics,29, 1958, p. 614.
 Walsh, J. E. “Computerfeasible method for handling incomplete data in regression analysis,”Journal of the Association for Computer Machinery,18, 1961, pp. 647–657.
 Wechsler, D.The Measurement and Appraisal of Adult Intelligence, Baltim, (4th edition), New York, 1958.
 Wilks, S. S. “Moments and distributions of estimates of population parameters from fragmentary samples,”Annals of Mathematical Statistics,3, 1932, pp. 163–195.
 Wold, H. “Nonlinear estimation by iterative least squares procedures,” Contribution inFestchrift Jerzy Neyman, F. N. David (Ed.), John Wiley and Sons, New York, 1966.
 Wold, H. “Estimation of principal components and related models,” Contribution inMultivariate Analysis, P. R. Krishnaiah (Ed.), Academic Press, New York, 1966.
 Title
 The estimation of variancecovariance and correlation matrices from incomplete data
 Journal

Psychometrika
Volume 35, Issue 4 , pp 417437
 Cover Date
 19701201
 DOI
 10.1007/BF02291818
 Print ISSN
 00333123
 Online ISSN
 18600980
 Publisher
 SpringerVerlag
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 Authors

 Neil H. Timm ^{(1)} ^{(2)}
 Author Affiliations

 1. Carnegie Commission on the Future of Higher Education, USA
 2. University of California, Berkeley