Introduction to Mixed Modelling - Beyond Regression and Analysis of Variance 2e, Galwey
Старое издание
Автор: Hocking Ronald R Название: Methods and Applications of Linear Models ISBN: 1118329503 ISBN-13(EAN): 9781118329504 Издательство: Wiley Рейтинг: Цена: 20109.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Praise for the Second Edition "An essential desktop reference book... it should definitely be on your bookshelf.
Автор: Alonso-gutierrez, David Bastero, Jesus Название: Approaching the kannan-lovasz-simonovits and variance conjectures ISBN: 3319132628 ISBN-13(EAN): 9783319132624 Издательство: Springer Рейтинг: Цена: 4890.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Focusing on two central conjectures of Asymptotic Geometric Analysis, the Kannan-Lovasz-Simonovits spectral gap conjecture and the variance conjecture, these Lecture Notes present the theory in an accessible way, so that interested readers, even those who are not experts in the field, will be able to appreciate the treated topics.
Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data.
New to the Second Edition
Reorganized to focus on unbalanced data
Reworked balanced analyses using methods for unbalanced data
Introductions to nonparametric and lasso regression
Introductions to general additive and generalized additive models
Examination of homologous factors
Unbalanced split plot analyses
Extensions to generalized linear models
R, Minitab(R), and SAS code on the author's website
The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.
Автор: Davies, Patrick Laurie Название: Data Analysis and Approximate Models ISBN: 1482215861 ISBN-13(EAN): 9781482215861 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Нет в наличии.
Автор: Lawal Bayo, Famoye Felix Название: Applied Statistics: Regression and Analysis of Variance ISBN: 0761861718 ISBN-13(EAN): 9780761861713 Издательство: Неизвестно Рейтинг: Цена: 24827.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a thorough treatment of the methods of regression and analysis of variance. Applied Statistics requires an understanding of introductory statistics courses and is suitable for both junior and senior undergraduate students.
Автор: Zhang, Jin-Ting Название: Analysis of Variance for Functional Data ISBN: 1439862737 ISBN-13(EAN): 9781439862735 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Нет в наличии.
Автор: Christensen Название: Analysis Of Variance, Design & Regr ISBN: 1498730140 ISBN-13(EAN): 9781498730143 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data.
New to the Second Edition
Reorganized to focus on unbalanced data
Reworked balanced analyses using methods for unbalanced data
Introductions to nonparametric and lasso regression
Introductions to general additive and generalized additive models
Examination of homologous factors
Unbalanced split plot analyses
Extensions to generalized linear models
R, Minitab(R), and SAS code on the author's website
The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.
Автор: Shaked, Moshe Название: Stable Non-Gaussian Random Processes ISBN: 0412051710 ISBN-13(EAN): 9780412051715 Издательство: Taylor&Francis Рейтинг: Цена: 29093.00 р. Наличие на складе: Нет в наличии.
Автор: Cox, D.r. Solomon, P.j. Название: Components of variance ISBN: 0367395975 ISBN-13(EAN): 9780367395971 Издательство: Taylor&Francis Рейтинг: Цена: 10104.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Identifying the sources and measuring the impact of haphazard variations are important in any number of research applications, from clinical trials and genetics to industrial design and psychometric testing. Only in very simple situations can such variations be represented effectively by independent, identically distributed random variables or by random sampling from a hypothetical infinite population. Components of Variance illuminates the complexities of the subject, setting forth its principles with focus on both the development of models for detailed analyses and the statistical techniques themselves. The authors first consider balanced and unbalanced situations, then move to the treatment of non-normal data, beginning with the Poisson and binomial models and followed by extensions to survival data and more general situations. In the final chapter, they discuss ways of extending and assessing various models, including the study of exceedances, the use of nonlinear representations, the study of transformations of the response variable, and the detailed examination of the distributional form of the underlying random variables. Careful signposting and numerous examples from genetic data analysis, clinical trial design, longitudinal data analysis, industrial design, and meta-analysis make this book accessible - and valuable - not only to statisticians but to all applied research scientists who use statistical methods.
Описание: The models studied are fractional Brownian motions and processes that derive from them through stochastic differential equations.Concerning the proofs of the limit theorems, the "Fourth Moment Theorem" is systematically used, as it produces rapid and helpful proofs that can serve as models for the future.
Автор: James D. Malley Название: Optimal Unbiased Estimation of Variance Components ISBN: 0387964495 ISBN-13(EAN): 9780387964492 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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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