000			02177nam a2200193 a 4500
020			_a9780387402727
020			_a0387402721
082			_a519.5 _bWAS
100			_aWasserman,Larry
245			_aAll of Statistics: A Concise Course in Statistical Inference-with 95 Figures
260			_aNew York : _bSpringer, _c©2004.
300			_axix, 442 pages : _billustrations ;
490			_aSpringer texts in statistics.
500			_aBibliography & index
505			_a I. Probability -- 1. Probability -- 2. Random Variables -- 3. Expectation -- 4. Inequalities -- 5. Convergence of Random Variables -- II. Statistical Inference -- 6. Models, Statistical Inference and Learning -- 7. Estimating the CDF and Statistical Functionals -- 8. The Bootstrap -- 9. Parametric Inference -- 10. Hypothesis Testing and p-values -- 11. Bayesian Inference -- 12. Statistical Decision Theory -- III. Statistical Models and Methods -- 13. Linear and Logistic Regression -- 14. Multivariate Models -- 15. Inference About Independence -- 16. Causal Inference -- 17. Directed Graphs and Conditional Independence -- 18. Undirected Graphs -- 19. Log-Linear Models -- 20. Nonparametric Curve Estimation -- 21. Smoothing Using Orthogonal Functions -- 22. Classification -- 23. Probability Redux: Stochastic Processes -- 24. Simulation Methods.
520			_a "This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining, and machine learning." "This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate levels."--Jacket.
650			_aMathematical statistics.
942			_cREF
999			_c40358 _d40358