Data Analysis / Wiley Series in Probability and Statistics (PDF)
What Can Be Learned From the Past 50 Years
(Sprache: Englisch)
This book explores the many provocative questions concerning the
fundamentals of data analysis. It is based on the time-tested
experience of one of the gurus of the subject matter. Why should
one study data analysis? How should it be taught? What...
fundamentals of data analysis. It is based on the time-tested
experience of one of the gurus of the subject matter. Why should
one study data analysis? How should it be taught? What...
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This book explores the many provocative questions concerning the
fundamentals of data analysis. It is based on the time-tested
experience of one of the gurus of the subject matter. Why should
one study data analysis? How should it be taught? What techniques
work best, and for whom? How valid are the results? How much data
should be tested? Which machine languages should be used, if used
at all? Emphasis on apprenticeship (through hands-on case studies)
and anecdotes (through real-life applications) are the tools that
Peter J. Huber uses in this volume. Concern with specific
statistical techniques is not of immediate value; rather, questions
of strategy - when to use which technique - are
employed. Central to the discussion is an understanding of the
significance of massive (or robust) data sets, the implementation
of languages, and the use of models. Each is sprinkled with an
ample number of examples and case studies. Personal practices,
various pitfalls, and existing controversies are presented when
applicable. The book serves as an excellent philosophical and
historical companion to any present-day text in data analysis,
robust statistics, data mining, statistical learning, or
computational statistics.
fundamentals of data analysis. It is based on the time-tested
experience of one of the gurus of the subject matter. Why should
one study data analysis? How should it be taught? What techniques
work best, and for whom? How valid are the results? How much data
should be tested? Which machine languages should be used, if used
at all? Emphasis on apprenticeship (through hands-on case studies)
and anecdotes (through real-life applications) are the tools that
Peter J. Huber uses in this volume. Concern with specific
statistical techniques is not of immediate value; rather, questions
of strategy - when to use which technique - are
employed. Central to the discussion is an understanding of the
significance of massive (or robust) data sets, the implementation
of languages, and the use of models. Each is sprinkled with an
ample number of examples and case studies. Personal practices,
various pitfalls, and existing controversies are presented when
applicable. The book serves as an excellent philosophical and
historical companion to any present-day text in data analysis,
robust statistics, data mining, statistical learning, or
computational statistics.
Autoren-Porträt von Peter J. Huber
Peter J. Huber, PhD, is a world-renowned statistician who has published four books and more than seventy journal articles in the areas of statistics and data analysis. He has held academic positions at Harvard University, Massachusetts Institute of Technology, Cornell University, and ETH Zurich (Switzerland), and has made significant research contributions in the areas of robust statistics, computational statistics, and strategies in data analysis. A Fellow of the Institute of Mathematical Statistics and the American Academy of Arts and Sciences, Dr. Huber is the coauthor of Robust Statistics, Second Edition, also published by Wiley.
Bibliographische Angaben
- Autor: Peter J. Huber
- 2011, 1. Auflage, 234 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1118018249
- ISBN-13: 9781118018248
- Erscheinungsdatum: 27.04.2011
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