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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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發(fā)表于 2025-3-21 20:06:04 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Exploring Multivariate Data with the Forward Search
編輯Anthony C. Atkinson,Marco Riani,Andrea Cerioli
視頻videohttp://file.papertrans.cn/320/319543/319543.mp4
概述Companion book to Robust Diagnostic Regression Analysis (ISBN 0-387-95017-6) published by Springer in 2000.Includes supplementary material:
叢書名稱Springer Series in Statistics
圖書封面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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0172-7397 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 inc978-1-4419-2353-0978-0-387-21840-3Series ISSN 0172-7397 Series E-ISSN 2197-568X
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Daniel M. Albert,Claudia Zrennere arbitrarily divided the variables into three groups. But do we need all the nine demographic variables in order to describe the variation in the communities or would a few variables suffice, or a few combinations of variables? Then the other variables would be contributing nothing but noise to the measurements.
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Spatial Linear Models,different treatments the effects of which are to be estimated. Another major example in environmental sciences is the study of pollution data recorded at a number of monitoring stations within the same area.
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https://doi.org/10.1007/978-94-011-1044-0of the data and then fit a multivariate normal distribution. This model can be fitted to any such rectangular array of numbers, so we need to explore the data to see whether this is an appropriate model for these data. Some departures from the multivariate normal model include:
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