Data Analysis
What Can Be Learned From the Past 50 Years
Published on 9. January 2012
234 pages
978-1-118-01826-2 (ISBN)
Description
This book explores the many provocative questions concerning thefundamentals of data analysis. It is based on the time-testedexperience of one of the gurus of the subject matter. Why shouldone study data analysis? How should it be taught? What techniqueswork best, and for whom? How valid are the results? How much datashould be tested? Which machine languages should be used, if usedat all? Emphasis on apprenticeship (through hands-on case studies)and anecdotes (through real-life applications) are the tools thatPeter J. Huber uses in this volume. Concern with specificstatistical techniques is not of immediate value; rather, questionsof strategy - when to use which technique - areemployed. Central to the discussion is an understanding of thesignificance of massive (or robust) data sets, the implementationof languages, and the use of models. Each is sprinkled with anample number of examples and case studies. Personal practices,various pitfalls, and existing controversies are presented whenapplicable. The book serves as an excellent philosophical andhistorical companion to any present-day text in data analysis,robust statistics, data mining, statistical learning, orcomputational statistics.
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Peter J. Huber
Data Analysis - What Can Be Learned From the Past 50 Years
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Person
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.
Content
Preface.
1 What is Data Analysis?
1.1 Tukey's 1962 paper.
1.2 The Path of Statistics.
2 Strategy Issues in Data Analysis.
2.1 Strategy in Data Analysis.
2.2 Philosophical issues.
2.3 Issues of size.
2.4 Strategic planning.
2.5 The stages of data analysis.
2.6 Tools required for strategy reasons.
3 Massive Data Sets.
3.1 Introduction.
3.2 Disclosure: Personal experiences.
3.3 What is i massive? A classification of size.
3.4 Obstacles to scaling.
3.5 On the structure of large data sets.
3.6 Data base management and related issues.
3.7 The stages of a data analysis.
3.8 Examples and some thoughts on strategy.
3.9 Volume reduction.
3.10 Supercomputers and software challenges.
3.11 Summary of conclusions.
4 Languages for Data Analysis.
4.1 Goals and purposes.
4.2 Natural languages and computing languages.
4.3 Interface issues.
4.4 Miscellaneous issues.
4.5 Requirements for a general purpose immediate language.
5 Approximate Models.
5.1 Models.
5.2 Bayesian modeling.
5.3 Mathematical statistics and approximate models.
5.4 Statistical significance and physical relevance.
5.5 Judicious use of a wrong model.
5.6 Composite models.
5.7 Modeling the length of day.
5.8 The role of simulation.
5.9 Summary of conclusions.
6 Pitfalls.
6.1 Simpson's paradox.
6.2 Missing data.
6.3 Regression of Y on X or of X onY.
7 Create order in data.
7.1 General considerations.
7.2 Principal component methods.
7.3 Multidimensional scaling.
7.4 Correspondence analysis.
7.5 Multidimensional scaling vs. Correspondence analysis.
8 More case studies.
8.1 A nutshell example.
8.2 Shape invariant modeling.
8.3 Comparison of point configurations.
8.4 Notes on numerical optimization.
References.
Index.
