MARC record from Internet Archive

LEADER: 03628cam 2200385 i 4500
001 9925307705801661
005 20170325050814.4
008 160912t20162016nyua b 001 0 eng
010 $a 2016028353
019 $a956745390$a959274071
020 $a9781107149892$q(hbk. ;$qalk. paper)
020 $a1107149894$q(hbk. ;$qalk. paper)
035 $a99975893597
035 $a(OCoLC)950929299$z(OCoLC)956745390$z(OCoLC)959274071
035 $a(OCoLC)ocn950929299
040 $aDLC$beng$erda$cDLC$dYDXCP$dYDX$dGRG$dNTD$dOCLCF$dBDX$dHEBIS$dHF9$dOCLCQ
042 $apcc
050 00 $aQA276.4$b.E376 2016
082 00 $a519.50285$223
100 1 $aEfron, Bradley,$eauthor.
245 10 $aComputer age statistical inference :$balgorithms, evidence, and data science /$cBradley Efron, Stanford University, California, Trevor Hastie, Stanford University, California.
264 1 $aNew York, NY :$bCambridge University Press,$c2016.
300 $axix, 475 pages :$bcolor illustrations ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
490 1 $aInstitute of Mathematical Statistics monographs ;$v5
504 $aIncludes bibliographical references and indexes.
505 0 $aPart I. Classic Statistical Inference: -- 1. Algorithms and inference -- 2. Frequentist inference -- 3. Bayesian inference -- 4. Fisherian inference and maximum likelihood estimation -- 5. Parametric models and exponential families -- Part II. Early Computer-Age Methods: -- 6. Empirical Bayes -- 7. James--Stein estimation and ridge regression -- 8. Generalized linear models and regression trees -- 9. Survival analysis and the EM algorithm -- 10. The jackknife and the bootstrap -- 11. Bootstrap confidence intervals -- 12. Cross-validation and Cp estimates of prediction error --13. Objective Bayes inference and Markov chain Monte Carlo --14. Statistical inference and methodology in the postwar era -- Part III. Twenty-First Century Topics: -- 15. Large-scale hypothesis testing and false discovery rates -- 16. Sparse modeling and the lasso -- 17. Random forests and boosting -- 18. Neural networks and deep learning -- 19. Support-vector machines and kernel methods -- 20. Inference after model selection -- 21. Empirical Bayes estimation strategies -- Epilogue.
520 $aThe twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science. -- Provided by publisher.
650 0 $aMathematical statistics$xData processing.
700 1 $aHastie, Trevor,$eauthor.
830 0 $aInstitute of Mathematical Statistics monographs ;$v5.
947 $hCIRCSTACKS$r31786103112279
980 $a99975893597