Previous |  Up |  Next

Article

Title: Modified minimax quadratic estimation of variance components (English)
Author: Witkovský, Viktor
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 34
Issue: 5
Year: 1998
Pages: [535]-543
Summary lang: English
.
Category: math
.
Summary: The paper deals with modified minimax quadratic estimation of variance and covariance components under full ellipsoidal restrictions. Based on the, so called, linear approach to estimation variance components, i. e. considering useful local transformation of the original model, we can directly adopt the results from the linear theory. Under normality assumption we can can derive the explicit form of the estimator which is formally find to be the Kuks–Olman type estimator. (English)
Keyword: ellipsoidal restrictions
MSC: 62C20
MSC: 62F10
MSC: 62F30
MSC: 62F35
MSC: 62J10
idZBL: Zbl 1274.62477
idMR: MR1663728
.
Date available: 2009-09-24T19:20:13Z
Last updated: 2015-03-28
Stable URL: http://hdl.handle.net/10338.dmlcz/135241
.
Reference: [1] Gaffke N., Heiligers B.: Bayes, admissible, and minimax linear estimators in linear models with restricted parameter space.Statistics 20 (1989), 4, 487–508 Zbl 0686.62019, MR 1047218, 10.1080/02331888908802199
Reference: [2] Heiligers B.: Linear Bayes and minimax estimation in linear models with partially restricted parameter space.J. Statist. Plann. Inference 36 (1993), 175–184 Zbl 0780.62027, MR 1234847, 10.1016/0378-3758(93)90122-M
Reference: [3] Kozák J.: Modified minimax estimation of regression coefficients.Statistics 16 (1985), 363–371 Zbl 0588.62108, MR 0792078, 10.1080/02331888508801866
Reference: [4] Kubáček L., Kubáčková L., Volaufová J.: Statistical Models with Linear Structures.Publishing House of the Slovak Academy of Sciences, Bratislava 1995
Reference: [5] Pilz J.: Minimax linear regression estimation with symmetric parameter restrictions.J. Statist. Plann. Inference 13 (1986), 297–318 Zbl 0602.62054, MR 0835614, 10.1016/0378-3758(86)90141-2
Reference: [6] Pukelsheim F.: Estimating variance components in linear models.J. Multivariate Anal. 6 (1976), 626–629 Zbl 0355.62061, MR 0438602, 10.1016/0047-259X(76)90010-5
Reference: [7] Rao C. R.: Estimation of variance and covariance components – MINQUE theory.J. Multivariate Anal. 1 (1971), 257–275 Zbl 0223.62086, MR 0301869, 10.1016/0047-259X(71)90001-7
Reference: [8] Rao C. R.: Minimum variance quadratic unbiased estimation of variance components.J. Multivariate Anal. 1 (1971), 445–456 Zbl 0259.62061, MR 0301870, 10.1016/0047-259X(71)90019-4
Reference: [9] Rao C. R.: Unified theory of linear estimation.Sankhyā Ser. B 33 (1971), 371–394 Zbl 0236.62048, MR 0319321
Reference: [10] Rao C. R., Kleffe J.: Estimation of Variance Components and Applications.Statistics and Probability, Volume 3. North–Holland, Amsterdam – New York – Oxford – Tokyo 1988 Zbl 0645.62073, MR 0933559
Reference: [11] Rao C. R., Mitra K.: Generalized Inverse of Matrices and Its Applications.Wiley, New York – London – Sydney – Toronto 1971 Zbl 0261.62051, MR 0338013
Reference: [12] Searle S. R., Casella, G., McCulloch, Ch. E.: Variance Components.(Wiley Series in Probability and Mathematical Statistics.) Wiley, New York – Chichester – Brisbane – Toronto – Singapore 1992 Zbl 1108.62064, MR 1190470
Reference: [13] Volaufová J.: A brief survey on the linear methods in variance-covariance components model.In: Model–Oriented Data Analysis (W. G. Müller, H. P. Wynn, and A. A. Zhigljavsky, eds.), Physica–Verlag, Heidelberg 1993, pp. 185–196 MR 1281860
Reference: [14] Volaufová J., Witkovský V.: Estimation of variance components in mixed linear model.Appl. Math. 37 (1992), 139–148
Reference: [15] Zyskind G.: On canonical forms, nonnegative covariance matrices and best and simple least square estimator in linear models.Ann. Math. Statist. 38 (1967), 1092–1110 MR 0214237, 10.1214/aoms/1177698779
.

Files

Files Size Format View
Kybernetika_34-1998-5_3.pdf 911.1Kb application/pdf View/Open
Back to standard record
Partner of
EuDML logo