MARC Record from marc_columbia

LEADER: 03089cam a22003974a 4500
001 5972486
005 20221121215841.0
008 030815t20042004nyua b 001 0 eng
010 $a 2003062209
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020 $a0387402721 (acid-free paper)
035 $a(OCoLC)ocm52901588
035 $a(NNC)5972486
035 $a5972486
040 $aDLC$cDLC$dOHX$dBAKER$dNLGGC$dOrLoB-B
042 $apcc
050 00 $aQA276.12$b.W37 2003
072 7 $aQA$2lcco
082 00 $a519.5$222
084 $a31.73$2bcl
100 1 $aWasserman, Larry,$d1959-$0http://id.loc.gov/authorities/names/n88665314
245 10 $aAll of statistics :$ba concise course in statistical inference /$cLarry Wasserman.
260 $aNew York :$bSpringer,$c[2004], ©2004.
300 $axix, 442 pages :$billustrations ;$c25 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aSpringer texts in statistics
504 $aIncludes bibliographical references (p. [423]-430) and index.
505 00 $gI.$tProbability --$g1.$tProbability --$g2.$tRandom Variables --$g3.$tExpectation --$g4.$tInequalities --$g5.$tConvergence of Random Variables --$gII.$tStatistical Inference --$g6.$tModels, Statistical Inference and Learning --$g7.$tEstimating the CDF and Statistical Functionals --$g8.$tThe Bootstrap --$g9.$tParametric Inference --$g10.$tHypothesis Testing and p-values --$g11.$tBayesian Inference --$g12.$tStatistical Decision Theory --$gIII.$tStatistical Models and Methods --$g13.$tLinear and Logistic Regression --$g14.$tMultivariate Models --$g15.$tInference About Independence --$g16.$tCausal Inference --$g17.$tDirected Graphs and Conditional Independence --$g18.$tUndirected Graphs --$g19.$tLog-Linear Models --$g20.$tNonparametric Curve Estimation --$g21.$tSmoothing Using Orthogonal Functions --$g22.$tClassification --$g23.$tProbability Redux: Stochastic Processes --$g24.$tSimulation Methods.
520 1 $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."--BOOK JACKET.
650 0 $aMathematical statistics.$0http://id.loc.gov/authorities/subjects/sh85082133
650 12 $aStatistics as Topic.$0https://id.nlm.nih.gov/mesh/D013223
650 17 $aStatistiek.$2gtt
830 0 $aSpringer texts in statistics.$0http://id.loc.gov/authorities/names/n84743107
852 00 $boff,bus$hQA276.12$i.W37 2003
852 00 $bhsl,stx$hQA276.12$i.W37 2003