Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems PDF Download
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Author: David A. Harville
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 105
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Book Description
Several recent developments promise to increase greatly the popularity of maximum likelihood (ML) as a technique for estimating variance components. Patterson and Thompson (Biometrika, Vol. 58, December 1971, pp. 545-554) proposed a restricted maximum likelihood (REML) approach which takes into account the loss in degrees of freedom resulting from estimating fixed effects. Miller (Technical Report No. 12, Department of Statistics, Stanford University, 1973) developed a realistic asymptotic theory for ML estimators of variance components. There are many iterative algorithms that can be considered for computing ML or REML estimates of variance components. Some were developed specifically for the variance component problem and related problems. Others are general nonlinear optimization procedures. The computations on each iteration of these algorithms are those associated with computing estimates of fixed and random effects for given values of the variance components. MINQUE's of variance components can be computed from one iteration of the REML version of Anderson's (Annals of Statistics, Vol. 1, January 1973, pp. 135-141) iterative procedures. (Author).
Author: David A. Harville
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 105
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Book Description
Several recent developments promise to increase greatly the popularity of maximum likelihood (ML) as a technique for estimating variance components. Patterson and Thompson (Biometrika, Vol. 58, December 1971, pp. 545-554) proposed a restricted maximum likelihood (REML) approach which takes into account the loss in degrees of freedom resulting from estimating fixed effects. Miller (Technical Report No. 12, Department of Statistics, Stanford University, 1973) developed a realistic asymptotic theory for ML estimators of variance components. There are many iterative algorithms that can be considered for computing ML or REML estimates of variance components. Some were developed specifically for the variance component problem and related problems. Others are general nonlinear optimization procedures. The computations on each iteration of these algorithms are those associated with computing estimates of fixed and random effects for given values of the variance components. MINQUE's of variance components can be computed from one iteration of the REML version of Anderson's (Annals of Statistics, Vol. 1, January 1973, pp. 135-141) iterative procedures. (Author).
Author: David A. Harville
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 105
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Book Description
Author: Michael A. Burgess
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 64
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Book Description
Author: Poduri S.R.S. Rao
Publisher: CRC Press
ISBN: 9780412728600
Category : Mathematics
Languages : en
Pages : 232
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Book Description
Variance Components Estimation deals with the evaluation of the variation between observable data or classes of data. This is an up-to-date, comprehensive work that is both theoretical and applied. Topics include ML and REML methods of estimation; Steepest-Acent, Newton-Raphson, scoring, and EM algorithms; MINQUE and MIVQUE, confidence intervals for variance components and their ratios; Bayesian approaches and hierarchical models; mixed models for longitudinal data; repeated measures and multivariate observations; as well as non-linear and generalized linear models with random effects.
Author: Karl-Rudolf Koch
Publisher: Springer Science & Business Media
ISBN: 3662039761
Category : Mathematics
Languages : en
Pages : 344
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Book Description
A treatment of estimating unknown parameters, testing hypotheses and estimating confidence intervals in linear models. Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for estimating random parameters. A chapter on the robust estimation of parameters and several examples have been added to this second edition. The necessary theorems of vector and matrix algebra and the probability distributions of test statistics are derived so as to make this book self-contained. Geodesy students as well as those in the natural sciences and engineering will find the emphasis on the geodetic application of statistical models extremely useful.
Author: Shayle R. Searle
Publisher: John Wiley & Sons
ISBN: 0470317698
Category : Mathematics
Languages : en
Pages : 537
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Book Description
WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. ". . .Variance Components is an excellent book. It is organized and well written, and provides many references to a variety of topics. I recommend it to anyone with interest in linear models." —Journal of the American Statistical Association "This book provides a broad coverage of methods for estimating variance components which appeal to students and research workers . . . The authors make an outstanding contribution to teaching and research in the field of variance component estimation." —Mathematical Reviews "The authors have done an excellent job in collecting materials on a broad range of topics. Readers will indeed gain from using this book . . . I must say that the authors have done a commendable job in their scholarly presentation." —Technometrics This book focuses on summarizing the variability of statistical data known as the analysis of variance table. Penned in a readable style, it provides an up-to-date treatment of research in the area. The book begins with the history of analysis of variance and continues with discussions of balanced data, analysis of variance for unbalanced data, predictions of random variables, hierarchical models and Bayesian estimation, binary and discrete data, and the dispersion mean model.
Author: Hardeo Sahai
Publisher: Springer Science & Business Media
ISBN: 0817644253
Category : Mathematics
Languages : en
Pages : 493
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Book Description
Systematic treatment of the commonly employed crossed and nested classification models used in analysis of variance designs with a detailed and thorough discussion of certain random effects models not commonly found in texts at the introductory or intermediate level. It also includes numerical examples to analyze data from a wide variety of disciplines as well as any worked examples containing computer outputs from standard software packages such as SAS, SPSS, and BMDP for each numerical example.
Author: Hongjian Yu
Publisher:
ISBN:
Category :
Languages : en
Pages : 194
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Book Description
Author: Shayle R. Searle
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 146
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Book Description
Author: Alice Marion Richardson
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 142
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Book Description
