Abstract
The maximum likelihood estimators are uniquely obtained in a multivariate normal distribution with AR(1) covariance structure for monotone data. The maximum likelihood estimator of mean is unbiased.
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References
Anderson T. W. (1957). Maximum likelihood estimators for a multivariate normal distribution when some observations are missing, J. Amer. Statist. Assoc., 52, 200–203.
Anderson T. W. and Olkin I. (1985). Maximum-likelihood estimation of the parameters of a multivariate normal distribution, Linear Algebra Appl., 70, 147–171.
Bhargava R. P. (1975). Some one-sample hypothesis testing problems when there is a monotone sample from a multivariate normal population, Ann. Inst. Statist. Math., 27, 327–339.
Dahiya R. C. and Korwar R. M. (1980). Maximum likelihood estimators for a bivariate normal distribution with missing data, Ann. Statist., 8, 687–692.
Fujisawa H. (1995). A note on the maximum likelihood estimators for multivariate normal distribution with monotone data, Comm. Statist. Theory Methods, 24, 1377–1382.
Jinadasa K. G. and Tracy D. S. (1992). Maximum likelihood estimation for multivariate normal distribution with monotone sample, Comm. Statist. Theory Methods, 21, 41–50.
Konishi S. and Shimizu K. (1994). Maximum likelihood estimation of an intraclass correlation in a bivariate normal distribution with missing observations, Comm. Statist. Theory Methods, 23, 1593–1604.
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Fujisawa, H. The maximum likelihood estimators in a multivariate normal distribution with AR(1) covariance structure for monotone data. Ann Inst Stat Math 48, 423–428 (1996). https://doi.org/10.1007/BF00050846
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DOI: https://doi.org/10.1007/BF00050846