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Titlebook: Exploring Multivariate Data with the Forward Search; Anthony C. Atkinson,Marco Riani,Andrea Cerioli Book 2004 Springer Science+Business Me

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書(shū)目名稱Exploring Multivariate Data with the Forward Search
編輯Anthony C. Atkinson,Marco Riani,Andrea Cerioli
視頻videohttp://file.papertrans.cn/321/320342/320342.mp4
概述Companion book to Robust Diagnostic Regression Analysis (ISBN 0-387-95017-6) published by Springer in 2000.Includes supplementary material:
叢書(shū)名稱Springer Series in Statistics
圖書(shū)封面Titlebook: Exploring Multivariate Data with the Forward Search;  Anthony C. Atkinson,Marco Riani,Andrea Cerioli Book 2004 Springer Science+Business Me
描述Why We Wrote This Book This book is about using graphs to explore and model continuous multi- variate data. Such data are often modelled using the multivariate normal distribution and, indeed, there is a literatme of weighty statistical tomes presenting the mathematical theory of this activity. Our book is very dif- ferent. Although we use the methods described in these books, we focus on ways of exploring whether the data do indeed have a normal distribution. We emphasize outlier detection, transformations to normality and the de- tection of clusters and unsuspected influential subsets. We then quantify the effect of these departures from normality on procedures such as dis- crimination and duster analysis. The normal distribution is central to our book because, subject to our exploration of departures, it provides useful models for many sets of data. However, the standard estimates of the parameters, especially the covari- ance matrix of the observations, are highly sensitive to the presence of outliers. This is both a blessing and a curse. It is a blessing because, if we estimate the parameters with the outliers excluded, their effect is appre- ciable and apparent if we then inc
出版日期Book 2004
關(guān)鍵詞Boxplot; Estimator; Excel; Linear discriminant analysis; Multivariate statistics; Normal distribution; Reg
版次1
doihttps://doi.org/10.1007/978-0-387-21840-3
isbn_softcover978-1-4419-2353-0
isbn_ebook978-0-387-21840-3Series ISSN 0172-7397 Series E-ISSN 2197-568X
issn_series 0172-7397
copyrightSpringer Science+Business Media New York 2004
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Springer Series in Statisticshttp://image.papertrans.cn/f/image/320342.jpg
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978-1-4419-2353-0Springer Science+Business Media New York 2004
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Tiziano Squartini,Diego GarlaschelliUnlike the other chapters in the book, this chapter contains little data analysis. The emphasis is on theory and on the description of the search. In the first half of the chapter we provide distributional results on estimation, testing and on the distribution of quantities such as squared Mahalanobis distances from samples of size ..
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The problem and basic properties, the Swiss heads data to exemplify the properties of bivariate boxplots for data analysis. As a preparation for material on transformations of data in Chapter 4 we compare analyses of the data on national track records for women when the response is the time for the race and also its reciprocal, spe
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Fundamental Theories of Physics why a transformation might be expected to be helpful in some examples. If the data arise from a counting process, they often have a Poisson distribution and the square root transformation will provide observations with an approximately constant variance, independent of the mean. Similarly, concentr
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https://doi.org/10.1007/978-94-010-9054-4th each other, so that not all are needed for a description of the subject of study; perhaps a few linear combinations of the variables would suffice. Other variables may be unrelated to any features of interest. The data on communities in Emilia-Romagna offer many such possibilities. In Chapter 4 w
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