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Titlebook: Linear Models and Generalizations; Least Squares and Al C. Radhakrishna Rao,Shalabh,Christian Heumann Textbook 2008Latest edition Springer-

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发表于 2025-3-21 17:42:20 | 显示全部楼层 |阅读模式
书目名称Linear Models and Generalizations
副标题Least Squares and Al
编辑C. Radhakrishna Rao,Shalabh,Christian Heumann
视频videohttp://file.papertrans.cn/587/586344/586344.mp4
概述Essential text for graduate statistics courses and courses where linear models play a part.Presents advanced research results and gives an overview of generalizations.New edition has been extensivley
丛书名称Springer Series in Statistics
图书封面Titlebook: Linear Models and Generalizations; Least Squares and Al C. Radhakrishna Rao,Shalabh,Christian Heumann Textbook 2008Latest edition Springer-
描述Thebookisbasedonseveralyearsofexperienceofbothauthorsinteaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and o?ers a selectionofclassicalandmodernalgebraicresultsthatareusefulinresearch work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results aboutthe de?niteness ofmatrices,especially forthe di?erences ofmatrices, which enable superiority comparisons of two biased estimates to be made for the ?rst time. We have attempted to provide a uni?ed theory of inference from linear models with minimal assumptions. Besides the usual least-squares theory, alternative methods of estimation and testing based on convex loss fu- tions and general estimating equations are discussed. Special emphasis is given to sensitivity anal
出版日期Textbook 2008Latest edition
关键词Fitting; Generalized linear model; Least Squares; Likelihood; Optimization Theory; Regression; best fit; ca
版次3
doihttps://doi.org/10.1007/978-3-540-74227-2
isbn_softcover978-3-642-09353-1
isbn_ebook978-3-540-74227-2Series ISSN 0172-7397 Series E-ISSN 2197-568X
issn_series 0172-7397
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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Springer Series in Statisticshttp://image.papertrans.cn/l/image/586344.jpg
Exposition
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无法解释
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The Multiple Linear Regression Model and Its Extensions,The main topic of this chapter is the linear regression model with more than one independent variables. The principles of . and . are used for the estimation of parameters. We present the algebraic, geometric, and statistical aspects of the problem, each of which has an intuitive appeal.
arrogant
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语源学
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Hemoptysis
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发表于 2025-3-22 17:49:52 | 显示全部楼层
Prediction in the Generalized Regression Model,68, 1970a, 1970b, 1970c). One of the main aims of the above publications is to examine the conditions under which biased estimators can lead to an improvement over conventional unbiased procedures. In the following, we will concentrate on recent results connected with alternative superiority criteria.
宽宏大量
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Sensitivity Analysis,en values of regressor variables. Methods for detecting outliers and deviation from normality of the distribution of errors are given in some detail. The material of this chapter is drawn mainly from the excellent book by Chatterjee and Hadi (1988).
熔岩
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Range-Of-Motion
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Models for Categorical Response Variables,ationship between the expectation of a response variable and unknown predictor variables according to . The parameters are estimated according to the principle of least squares and are optimal according to minimum dispersion theory, or in case of a normal distribution, are optimal according to the ML theory (cf. Chapter 3).
Hallmark
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