000			03345nam a22005775i 4500
001			978-3-642-34333-9
003			DE-He213
005			20140220082857.0
007			cr nn 008mamaa
008			130509s2013 gw | s |||| 0|eng d
020			_a9783642343339 _9978-3-642-34333-9
024	7		_a10.1007/978-3-642-34333-9 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aK _2bicssc
072		7	_aBUS061000 _2bisacsh
082	0	4	_a330.015195 _223
100	1		_aFahrmeir, Ludwig. _eauthor.
245	1	0	_aRegression _h[electronic resource] : _bModels, Methods and Applications / _cby Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aXIV, 698 p. 204 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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		0	_aStatistics.
650		0	_aEpidemiology.
650		0	_aBioinformatics.
650		0	_aStatistical methods.
650		0	_aMathematical statistics.
650		0	_aEconomics _xStatistics.
650		0	_aEconometrics.
650	1	4	_aStatistics.
650	2	4	_aStatistics for Business/Economics/Mathematical Finance/Insurance.
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aEconometrics.
650	2	4	_aBiostatistics.
650	2	4	_aBioinformatics.
650	2	4	_aEpidemiology.
700	1		_aKneib, Thomas. _eauthor.
700	1		_aLang, Stefan. _eauthor.
700	1		_aMarx, Brian. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642343322
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-34333-9
912			_aZDB-2-SMA
999			_c97499 _d97499