Optimal Unbiased Estimation Variance Components by Malley James (18 results)

Condition: Very Good. *FREE DOMESTIC SHIPPING UNTIL MONDAY, NOV. 24* 146 pp., paperback, very good. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Paperback. Condition: Very Good. Text is unmarked; pages are lightly yellowed. Binding is tight and square. covers are very slightly edgeworn and there is a big scuff mark/scar on the front cover. 146pp.

paperback. Condition: Good. Good. Used with wear and age toning to pages but is still in solid reading condition. Pasadena's finest new and used bookstore since 1992.

Paper Bound. Condition: Very Good to Fine. Sun-faded spine.

Condition: New.

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.

Condition: New. In.

Condition: New. pp. 160.

Paperback. Condition: Brand New. 156 pages. 9.25x6.50x0.50 inches. In Stock.

Taschenbuch. Condition: 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.

Softcover. Condition: Very Good. 146 pages in very good condition. Pages are clean and unmarked. Bound in orange and black card covers with black titles. Lightly worn on the edges. VG. Book.

Taschenbuch. Condition: 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.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 302.

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.

Condition: New. PRINT ON DEMAND pp. 160.

Condition: 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.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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.