All of Statistics: A Concise Course in Statistical InferenceTaken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con ducted in statistics departments while data mining and machine learning re search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo rithms are more scalable than statisticians ever thought possible. Formal sta tistical theory is more pervasive than computer scientists had realized. |
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All of Statistics: A Concise Course in Statistical Inference Larry Wasserman No preview available - 2010 |
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95 percent confidence analysis approximate assume Bayes Bayesian bootstrap called chapter choose classifier coin Compare compute conditional consistent constant construct continuous convergence corresponds covariates defined Definition denote density depend discuss disease distribution draw estimate event Example expectation fact Figure Find fixed function given graph Hence histogram hypothesis implies independent inequality inference integral Let X Let X1 likelihood limit linear Markov chain matrix mean measure method minimize nonparametric Normal Note null observations otherwise outcome p-value parameter plot posterior prior probability problem PROOF Prove random variables regression reject result risk rule sample Show shown simulation smooth ſº space squared standard error statistic steps sufficient Suppose Theorem theory treatment true variance vector versus write written
Bibliographic information
| Title | All of Statistics: A Concise Course in Statistical Inference Springer Texts in Statistics |
| Author | Larry Wasserman |
| Edition | illustrated |
| Publisher | Springer Science & Business Media, 2013 |
| ISBN | 0387217363, 9780387217369 |
| Length | 442 pages |
| Subjects | › › Computers / Mathematical & Statistical Software Mathematics / Counting & Numeration Mathematics / Discrete Mathematics Mathematics / Numerical Analysis Mathematics / Probability & Statistics / General Mathematics / Probability & Statistics / Stochastic Processes Science / Physics / General |
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