Optimal Unbiased Estimation of Variance Components, James D. Malley
Автор: V.G. Voinov; M.S. Nikulin Название: Unbiased Estimators and their Applications ISBN: 0792339398 ISBN-13(EAN): 9780792339397 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Contains problems of parametric point estimation for multivariate probability distributions emphasizing problems of unbiased estimation. This book covers some basic properties of multivariate continuous and discrete distributions, the general theory of point estimation in multivariate case, and techniques for constructing unbiased estimators.
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.
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.
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.
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