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000696185 019__ $$a863638611
000696185 020__ $$a9783642378874 $$qelectronic book
000696185 020__ $$a3642378870 $$qelectronic book
000696185 020__ $$z9783642378867
000696185 020__ $$z3642378862
000696185 0247_ $$a10.1007/978-3-642-37887-4$$2doi
000696185 035__ $$aSP(OCoLC)ocn866922472
000696185 035__ $$aSP(OCoLC)866922472$$z(OCoLC)863638611
000696185 040__ $$aYDXCP$$cYDXCP$$dOCLCO$$dGW5XE$$dCOO
000696185 049__ $$aISEA
000696185 050_4 $$aQA276$$b.H45 2014
000696185 08204 $$a519.5/4$$223
000696185 1001_ $$aHeld, Leonhard,$$eauthor.
000696185 24510 $$aApplied statistical inference$$h[electronic resource] :$$blikelihood and Bayes /$$cLeonhard Held, Daniel Sabanés Bové.
000696185 264_1 $$aBerlin :$$bSpringer,$$c[2014]
000696185 300__ $$a1 online resource (xiii, 376 pages.)
000696185 336__ $$atext$$btxt$$2rdacontent
000696185 337__ $$acomputer$$bc$$2rdamedia
000696185 338__ $$aonline resource$$bcr$$2rdacarrier
000696185 504__ $$aIncludes bibliographical references and index.
000696185 506__ $$aAccess limited to authorized users.
000696185 520__ $$aThis book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective. A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis.
000696185 588__ $$aDescription based on print version record.
000696185 650_0 $$aMathematical statistics.
000696185 650_0 $$aBayesian statistical decision theory.
000696185 650_0 $$aEstimation theory.
000696185 7001_ $$aSabanés Bové, Daniel,$$eauthor.
000696185 7001_ $$iexpanded version of (work)$$aHeld, Leonhard.$$tMethoden der statistischen Inferenz.
000696185 77608 $$cOriginal$$z9783642378867$$z3642378862
000696185 85280 $$bebk$$hSpringerLink
000696185 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-642-37887-4$$zOnline Access
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000696185 980__ $$aEBOOK
000696185 980__ $$aBIB
000696185 982__ $$aEbook
000696185 983__ $$aOnline
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