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The clearest way into the Universe is through a forest wilderness. John MuIr As recently as 1970 the problem of obtaining optimal estimates for variance components in a mixed linear model with unbalanced data was considered a miasma of competing, generally weakly motivated estimators, with few firm gUidelines and many simple, compelling but Unanswered questions. Then in 1971 two significant beachheads were secured: the results of Rao [1971a, 1971b] and his MINQUE estimators, and related to these but not originally derived from them, the results of Seely [1971] obtained as part of his introduction of the no~ion of quad ratic subspace into the literature of variance component estimation. These two approaches were ultimately shown to be intimately related by Pukelsheim [1976], who used a linear model for the com ponents given by Mitra [1970], and in so doing, provided a mathemati cal framework for estimation which permitted the immediate applica tion of many of the familiar Gauss-Markov results, methods which had earlier been so successful in the estimation of the parameters in a linear model with only fixed effects. Moreover, this usually enor mous linear model for the components can be displayed as the starting point for many of the popular variance component estimation tech niques, thereby unifying the subject in addition to generating answers.
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The clearest way into the Universe is through a forest wilderness. John MuIr As recently as 1970 the problem of obtaining optimal estimates for variance components in a mixed linear model with unbalanced data was considered a miasma of competing, generally weakly motivated estimators, with few firm gUidelines and many simple, compelling but Unanswered questions. Then in 1971 two significant beachheads were secured: the results of Rao [1971a, 1971b] and his MINQUE estimators, and related to these but not originally derived from them, the results of Seely [1971] obtained as part of his introduction of the no~ion of quad ratic subspace into the literature of variance component estimation. These two approaches were ultimately shown to be intimately related by Pukelsheim [1976], who used a linear model for the com ponents given by Mitra [1970], and in so doing, provided a mathemati cal framework for estimation which permitted the immediate applica tion of many of the familiar Gauss-Markov results, methods which had earlier been so successful in the estimation of the parameters in a linear model with only fixed effects. Moreover, this usually enor mous linear model for the components can be displayed as the starting point for many of the popular variance component estimation tech niques, thereby unifying the subject in addition to generating answers.
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Optimal Unbiased Estimation of Variance Components
Verlag:
Springer-Verlag New York Inc., 1986
ISBN 10: 0387964495
ISBN 13: 9780387964492
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Paperback. Zustand: Fair. Titel: Optimal Unbiased Estimation of Variance Components. Jaar van uitgave: 1986. Taal: Engels. Kaft iets verbleekt en hoekjes iets gebogen. Pagina's wat vergeeld verder prima. Enkele gebruik-/opslagsporen. Bestandsnummer des Verkäufers 88968
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Optimal Unbiased Estimation of Variance Components
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Optimal Unbiased Estimation of Variance Components
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. One: The Basic Model and the Estimation Problem.- 1.1 Introduction.- 1.2 An Example.- 1.3 The Matrix Formulation.- 1.4 The Estimation Criteria.- 1.5 Properties of the Criteria.- 1.6 Selection of Estimation Criteria.- Two: Basic Linear Technique.- 2.1 Introd. Bestandsnummer des Verkäufers 5912743
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Optimal Unbiased Estimation of Variance Components
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Springer New York, Springer New York Dez 1986, 1986
ISBN 10: 0387964495
ISBN 13: 9780387964492
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Taschenbuch. Zustand: Neu. Neuware -The clearest way into the Universe is through a forest wilderness. John MuIr As recently as 1970 the problem of obtaining optimal estimates for variance components in a mixed linear model with unbalanced data was considered a miasma of competing, generally weakly motivated estimators, with few firm gUidelines and many simple, compelling but Unanswered questions. Then in 1971 two significant beachheads were secured: the results of Rao [1971a, 1971b] and his MINQUE estimators, and related to these but not originally derived from them, the results of Seely [1971] obtained as part of his introduction of the no~ion of quad ratic subspace into the literature of variance component estimation. These two approaches were ultimately shown to be intimately related by Pukelsheim [1976], who used a linear model for the com ponents given by Mitra [1970], and in so doing, provided a mathemati cal framework for estimation which permitted the immediate applica tion of many of the familiar Gauss-Markov results, methods which had earlier been so successful in the estimation of the parameters in a linear model with only fixed effects. Moreover, this usually enor mous linear model for the components can be displayed as the starting point for many of the popular variance component estimation tech niques, thereby unifying the subject in addition to generating answers.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 160 pp. Englisch. Bestandsnummer des Verkäufers 9780387964492
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Optimal Unbiased Estimation of Variance Components
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The clearest way into the Universe is through a forest wilderness. John MuIr As recently as 1970 the problem of obtaining optimal estimates for variance components in a mixed linear model with unbalanced data was considered a miasma of competing, generally weakly motivated estimators, with few firm gUidelines and many simple, compelling but Unanswered questions. Then in 1971 two significant beachheads were secured: the results of Rao [1971a, 1971b] and his MINQUE estimators, and related to these but not originally derived from them, the results of Seely [1971] obtained as part of his introduction of the no~ion of quad ratic subspace into the literature of variance component estimation. These two approaches were ultimately shown to be intimately related by Pukelsheim [1976], who used a linear model for the com ponents given by Mitra [1970], and in so doing, provided a mathemati cal framework for estimation which permitted the immediate applica tion of many of the familiar Gauss-Markov results, methods which had earlier been so successful in the estimation of the parameters in a linear model with only fixed effects. Moreover, this usually enor mous linear model for the components can be displayed as the starting point for many of the popular variance component estimation tech niques, thereby unifying the subject in addition to generating answers. 160 pp. Englisch. Bestandsnummer des Verkäufers 9780387964492
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Optimal Unbiased Estimation of Variance Components
Verlag:
Springer New York, Springer New York, 1986
ISBN 10: 0387964495
ISBN 13: 9780387964492
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The clearest way into the Universe is through a forest wilderness. John MuIr As recently as 1970 the problem of obtaining optimal estimates for variance components in a mixed linear model with unbalanced data was considered a miasma of competing, generally weakly motivated estimators, with few firm gUidelines and many simple, compelling but Unanswered questions. Then in 1971 two significant beachheads were secured: the results of Rao [1971a, 1971b] and his MINQUE estimators, and related to these but not originally derived from them, the results of Seely [1971] obtained as part of his introduction of the no~ion of quad ratic subspace into the literature of variance component estimation. These two approaches were ultimately shown to be intimately related by Pukelsheim [1976], who used a linear model for the com ponents given by Mitra [1970], and in so doing, provided a mathemati cal framework for estimation which permitted the immediate applica tion of many of the familiar Gauss-Markov results, methods which had earlier been so successful in the estimation of the parameters in a linear model with only fixed effects. Moreover, this usually enor mous linear model for the components can be displayed as the starting point for many of the popular variance component estimation tech niques, thereby unifying the subject in addition to generating answers. Bestandsnummer des Verkäufers 9780387964492
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Optimal Unbiased Estimation of Variance Components (Lecture Notes in Statistics, 39)
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Optimal Unbiased Estimation of Variance Components
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Springer-Verlag, Berlin, 1986
ISBN 10: 0387964495
ISBN 13: 9780387964492
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Optimal Unbiased Estimation of Variance Components
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ISBN 10: 0387964495
ISBN 13: 9780387964492
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Optimal Unbiased Estimation of Variance Components
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Zustand: New. PRINT ON DEMAND pp. 160. Bestandsnummer des Verkäufers 183868775
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