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Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: 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.". Buchnummer des Verkäufers

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Inhaltsangabe: 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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James D. Malley
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Buchbeschreibung Springer-Verlag New York Inc., 1986. PAP. Buchzustand: New. New Book. Delivered from our UK warehouse in 3 to 5 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Buchnummer des Verkäufers LQ-9780387964492

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Buchbeschreibung Springer-Verlag New York Inc., United States, 1986. Paperback. Buchzustand: New. 244 x 170 mm. Language: English . Brand New Book ***** Print on Demand *****.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. Softcover reprint of the original 1st ed. 1986. Buchnummer des Verkäufers AAV9780387964492

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Buchbeschreibung Springer, 1986. Paperback. Buchzustand: NEW. 9780387964492 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Buchnummer des Verkäufers HTANDREE0276125

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Buchbeschreibung Springer-Verlag New York Inc., United States, 1986. Paperback. Buchzustand: New. 244 x 170 mm. Language: English . Brand New Book ***** Print on Demand *****. 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. Softcover reprint of the original 1st ed. 1986. Buchnummer des Verkäufers AAV9780387964492

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Buchbeschreibung Springer-Verlag New York Inc., 1986. PAP. Buchzustand: New. New Book. Shipped from US within 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Buchnummer des Verkäufers IQ-9780387964492

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Buchbeschreibung Springer, 1986. Buchzustand: New. This item is printed on demand for shipment within 3 working days. Buchnummer des Verkäufers LP9780387964492

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Buchbeschreibung Springer, 1986. Paperback. Buchzustand: New. This item is printed on demand. Buchnummer des Verkäufers INGM9780387964492

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Buchbeschreibung Springer. Paperback. Buchzustand: New. Paperback. 160 pages. Dimensions: 9.6in. x 6.7in. x 0.4in.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 noion 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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Buchnummer des Verkäufers 9780387964492

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Buchbeschreibung Springer, 1986. Paperback. Buchzustand: New. 1. This item is printed on demand. Buchnummer des Verkäufers DADAX0387964495

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