| Exploring Multivariate Data with the Forward Search 2004 Edition Contributor(s): Atkinson, Anthony C. (Author), Riani, Marco (Author), Cerioli, Andrea (Author) |
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ISBN: 0387408525 ISBN-13: 9780387408521 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: January 2004 Annotation: This book is concerned with data in which the observations are independent and in which the response is multivariate. Anthony Atkinson has been Professor of Statistics at the London School of Economics since 1989. Before that he was a Professor at Imperial College, London. He is the author of Plots, Transformations, and Regression, co-author of Optimum Experimental Designs, and joint editor of The Fascination of Statistics, a volume celebrating the centenary of the International Statistical Institute. Professor Atkinson has served as editor of The Journal of the Royal Statistical Society, Series B and as associate editor of Biometrika and Technometrics. He has published well over 100 articles in these and other journals including The Annals of Statistics, Biometrics, The Journal of the American Statistical Association, and Statistics and Computing. Marco Riani, after receiving his Ph.D. in Statistics in 1995 from the University of Florence, joined the Faculty of Economics at Parma University as postdoctoral fellow. In 1997 he won the prize for the best Italian Ph.D. thesis in Statistics. He is currently Associate Professor of Statistics in the University of Parma. He has published in Technometrics, The Journal of Computational and Graphical Statistics, The Journal of Business and Economic Statistics, The Journal of Forecasting, Environmetrics, Computational Statistics and Data Analysis, Metron, and other journals. From the reviews: "The book requires knowledge of multivariate statistical methods, because it provides only basic background information on the methods considered (although with excellent references for futher reading at the end of each chapter). Each chapteralso includes exercises with solutions...This book could serve as an excellent text for an advanced course on modern multivariate statistics, as it is intended." Technometrics, November 2004 "This book is full of interest for anyone undertaking multivariate analyses, clearly emphasizing that uncritical use of standard methods can be misleading." Short Book Reviews of the International Statistical Institute, December 2004 "This book is an interesting complement to various textbooks on multivariate statistics." Biometrics, December 2005 "This book discusses multivariate data from a different perspective. ??? it is an excellent book for researchers with interests in multivariate data and cluster analysis. It may also be a good reference for students of advanced statistics and practitioners working with large volumes of data ??? ." (Kassim S. Mwitondi, Journal of Applied Statistics, Vol. 32 (4), 2005) "This is a companion to an earlier book ??? both of which feature many informative graphs. Here, the forward search has been applied in detail to classical multivariate approaches used with Gaussian data. ??? One valuable feature of the book is the way that the illustrations concentrate on a relatively small number ??? . This makes it easy to concentrate on the application ??? . The implications of this book also strengthen the importance of data visualization, as well as providing a valuable approach to visualization." (Paul Hewson, Journal of the Royal Statistical Society Series A, Vol. 168 (2), 2005) "This book is a companion to Atkinson ??? . The objective is to identify outliers, appreciate their influence ??? which would result in an overall improvement. ??? Graphicaltools are widely used, resulting in three hundred and ninety figures. Each chapter is followed by extensive exercises and their solutions, and the book could be used as an advanced textbook for multivariate analysis courses. Web-sites provide the relevant software ??? . This book is full of interest for anyone undertaking multivariate analyses ??? ." (B.J.T. Morgan, Short Book Reviews International Statistical Institute, Vol. 24 (3), 2004) "This book discusses forward search (FS), a method using graphs to explore and model continuous multivariate data ??? . Its viewpoint is toward applications, and it demonstrates the merits of FS using a variety of examples, with a thorough discussion of statistical issues and interpretation of results. ??? This book could serve as an excellent text for an advanced course on modern multivariate statistics, as it is intended." (Tena Ipsilantis Katsaounis, Technometrics, Vol. 46 (4), November, 2004) "The theoretical exercises with detailed solutions at the end of each chapter are extremely useful. I would recommend this book to practitioners who analyze moderately sized multivariate data. Of course, anyone associated with the application of statistics should find the book interesting to read." (Tathgata Banerjee, Journal of the American Statistical Association, March 2006) |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Multivariate Analysis |
| Dewey: 519.535 |
| LCCN: 2003058614 |
| Series: Springer Series in Statistics |
| Physical Information: 1.34" H x 6.38" W x 9.5" (2.24 lbs) 624 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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 include them for estimation. It is however a curse because it can be hard to detect which observations are outliers. We use the forward search for this purpose. |