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.
"synopsis" may belong to another edition of this title.
Seller: Feldman's Books, Menlo Park, CA, U.S.A.
Paper Bound. Condition: Very Good to Fine. Sun-faded spine. Seller Inventory # 00040274
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condition: new. Paperback. 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. 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 quadA 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 comA ponents given by Mitra [1970], and in so doing, provided a mathematiA cal framework for estimation which permitted the immediate applicaA 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 enorA mous linear model for the components can be displayed as the starting point for many of the popular variance component estimation techA niques, thereby unifying the subject in addition to generating an Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780387964492
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New. Seller Inventory # 19458508-n
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Condition: New. Seller Inventory # ABLIING23Feb2215580174780
Seller: p015, Rotterdam, Netherlands
Paperback. Condition: 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. Seller Inventory # 88968
Quantity: 1 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition. Seller Inventory # 19458508
Seller: Antiquariat Renner OHG, Albstadt, Germany
Softcover. Condition: Sehr gut. Berlin, Springer (1986). gr.8°. IX, 146 p. Pbck. Lecture Notes on Statistics, 39.- Throughout browned. Seller Inventory # 94713
Quantity: 1 available
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Condition: New. In. Seller Inventory # ria9780387964492_new
Quantity: Over 20 available
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. pp. 160. Seller Inventory # 263868781
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand pp. 160 67:B&W 6.69 x 9.61 in or 244 x 170 mm (Pinched Crown) Perfect Bound on White w/Gloss Lam. Seller Inventory # 5027762
Quantity: 4 available