MARC Record from harvard_bibliographic_metadata

LEADER: 03655nam a22004575a 4500
001 014157649-9
005 20141003190123.0
008 131129s2004 xxu| o ||0| 0|eng d
020 $a9780387217369
020 $a9781441923226 (ebk.)
020 $a9780387217369
020 $a9781441923226
024 7 $a10.1007/978-0-387-21736-9$2doi
035 $a(Springer)9780387217369
040 $aSpringer
050 4 $aQA276-280
072 7 $aMAT029000$2bisacsh
072 7 $aPBT$2bicssc
082 04 $a519.5$223
100 1 $aWasserman, Larry,$eauthor.
245 10 $aAll of Statistics :$bA Concise Course in Statistical Inference /$cby Larry Wasserman.
264 1 $aNew York, NY :$bSpringer New York :$bSpringer,$c2004.
300 $aXIX, 442 p.$bonline resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file$bPDF$2rda
490 1 $aSpringer Texts in Statistics,$x1431-875X
505 0 $aProbability -- Random Variables -- Expectation -- Inequalities -- Convergence of Random Variables -- Models, Statistical Inference and Learning -- Estimating the CDF and Statistical Functionals -- The Bootstrap -- Parametric Inference -- Hypothesis Testing and p-values -- Bayesian Inference -- Statistical Decision Theory -- Linear and Logistic Regression -- Multivariate Models -- Inference about Independence -- Causal Inference -- Directed Graphs and Conditional Independence -- Undirected Graphs -- Loglinear Models -- Nonparametric Curve Estimation -- Smoothing Using Orthogonal Functions -- Classification -- Probability Redux: Stochastic Processes -- Simulation Methods.
520 $aThis 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 level. Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal–Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.
650 10 $aStatistics.
650 0 $aComputer science.
650 0 $aMathematical statistics.
650 0 $aStatistics.
650 24 $aProbability and Statistics in Computer Science.
650 24 $aStatistical Theory and Methods.
650 24 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
776 08 $iPrinted edition:$z9781441923226
830 0 $aSpringer Texts in Statistics.
988 $a20140910
906 $0VEN