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eBook The Coordinate-Free Approach to Linear Models (Cambridge Series in Statistical and Probabilistic Mathematics) ePub

eBook The Coordinate-Free Approach to Linear Models (Cambridge Series in Statistical and Probabilistic Mathematics) ePub

by Michael J. Wichura

  • ISBN: 0521868424
  • Category: Mathematics
  • Subcategory: Math Science
  • Author: Michael J. Wichura
  • Language: English
  • Publisher: Cambridge University Press; 1 edition (October 23, 2006)
  • Pages: 214
  • ePub book: 1556 kb
  • Fb2 book: 1688 kb
  • Other: txt rtf azw doc
  • Rating: 4.7
  • Votes: 449

Description

The coordinate-free, or geometric, approach to the theory of linear models is more insightful, more elegant, more direct, and simpler than the more common matrix approach.

The coordinate-free, or geometric, approach to the theory of linear models is more insightful, more elegant, more direct, and simpler than the more common matrix approach. This book treats Model I ANOVA and linear regression models with non-random predictors in a finite-dimensional setting. Series: Cambridge Series in Statistical and Probabilistic Mathematics (Book 19). Hardcover: 214 pages. It is a required textbook, but still very hard to understand~ And there are some typos in the exe, which makes it very confusing.

50 results in Cambridge Series in Statistical and Probabilistic Mathematics. Relevance Title Sorted by Date. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference.

Start by marking Coordinate-Free Approach to Linear Models, Th. This book is about the coordinate-free, or geometric, approach to the theory of linear models; more precisely, Model I ANOVA and linear regression models with non-random predictors in a finite-dimensional setting.

Start by marking Coordinate-Free Approach to Linear Models, The. Cambridge Series in Statistical and Probabilistic Mathematics as Want to Read: Want to Read savin. ant to Read.

Chapter two goes through the standard linear regression models and the diagnostic checks for those models. They also cover other practical issues including model selection, use of transformations and extensions to nonlinear models. The special case of polynomial regression (a particular example of linear regression) is presented in detail. Chapter 3 on scatterplot smoothing introduces many of the key ideas to their approach to semiparametric regression

The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference Cambridge Series on Statistical & Probabilistic Mathematics.

The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference Cambridge Series on Statistical & Probabilistic Mathematics. 47 MB·244 Downloads·New! presents a philosophical critique of early ideas about probability, induction, and statistical inference.

Автор: Michael J. Wichura Название: The Coordinate-Free Approach to Linear Models Издательство: Cambridge . It also includes a chapter on the statistical background and one on useful software.

It also includes a chapter on the statistical background and one on useful software.

Электронная книга "The Coordinate-Free Approach to Linear Models", Michael J. Wichura. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "The Coordinate-Free Approach to Linear Models" для чтения в офлайн-режиме.

Unfortunately the book is not well written and that is the main reason why it is not more popular than it is. The text beyond first three chapters is largely useless and hard to sort out, with very little care about readability

Unfortunately the book is not well written and that is the main reason why it is not more popular than it is. The text beyond first three chapters is largely useless and hard to sort out, with very little care about readability. There are moments in the text when the author assumes his reader is quite telepatic as some proofs are rather sketchy, and some are even erroneous. The book also contains quite a few misprints adding to confusion.

This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook.

Semantic Scholar extracted view of "Cambridge Series in Statistical and Probabilistic Mathematics" by. .oceedings{I, title {Cambridge Series in Statistical and Probabilistic Mathematics}, author {Robert Norris}, year {1997} }. Robert Norris.

Semantic Scholar extracted view of "Cambridge Series in Statistical and Probabilistic Mathematics" by Robert Norris.

This book is about the coordinate-free, or geometric, approach to the theory of linear models; more precisely, Model I ANOVA and linear regression models with nonrandom predictors in a finite-dimensional setting. This approach is more insightful, more elegant, more direct, and simpler than the more common matrix approach to linear regression, analysis of variance, and analysis of covariance models in statistics. The book discusses the intuition behind and optimal properties of various methods of estimating and testing hypotheses about unknown parameters in the models. Topics covered include inner product spaces, orthogonal projections, book orthogonal spaces, Tjur experimental designs, basic distribution theory, the geometric version of the Gauss-Markov theorem, optimal and nonoptimal properties of Gauss-Markov, Bayes, and shrinkage estimators under the assumption of normality, the optimal properties of F-tests, and the analysis of covariance and missing observations.