MARC Record from harvard_bibliographic_metadata

LEADER: 02957nam a22005055a 4500
001 013699736-8
005 20130712192652.0
008 130509s2013 gw | s ||0| 0|eng d
020 $a9783642343339
020 $a9783642343339
020 $a9783642343322
024 7 $a10.1007/978-3-642-34333-9$2doi
035 $a(Springer)9783642343339
040 $aSpringer
050 4 $aQA276-280
072 7 $aPBT$2bicssc
072 7 $aK$2bicssc
072 7 $aBUS061000$2bisacsh
082 04 $a330.015195$223
100 1 $aFahrmeir, Ludwig.
245 10 $aRegression :$bModels, Methods and Applications /$cby Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx.
260 $aBerlin, Heidelberg :$bSpringer Berlin Heidelberg :$bImprint: Springer,$c2013.
300 $aXIV, 698 p. 204 illus.$bdigital.
505 0 $aIntroduction -- Regression Models -- The Classical Linear Model -- Extensions of the Classical Linear Model -- Generalized Linear Models -- Categorical Regression Models -- Mixed Models -- Nonparametric Regression -- Structured Additive Regression -- Quantile Regression -- A Matrix Algebra -- B Probability Calculus and Statistical Inference -- Bibliography -- Index.
520 $aThe aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.
650 20 $aEpidemiology.
650 20 $aBioinformatics.
650 20 $aBiometry.
650 20 $aEconometrics.
650 10 $aStatistics.
650 0 $aStatistics.
650 0 $aEpidemiology.
650 0 $aBioinformatics.
650 0 $aStatistical methods.
650 0 $aMathematical statistics.
650 0 $aEconomics$xStatistics.
650 0 $aEconometrics.
650 24 $aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 24 $aStatistical Theory and Methods.
700 1 $aKneib, Thomas.
700 1 $aLang, Stefan.
700 1 $aMarx, Brian.
776 08 $iPrinted edition:$z9783642343322
988 $a20130604
906 $0VEN