000			02947nam a22004455i 4500
001			978-1-4419-0925-1
003			DE-He213
005			20140220082802.0
007			cr nn 008mamaa
008			130104s2013 xxu| s |||| 0|eng d
020			_a9781441909251 _9978-1-4419-0925-1
024	7		_a10.1007/978-1-4419-0925-1 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aWakefield, Jon. _eauthor.
245	1	0	_aBayesian and Frequentist Regression Methods _h[electronic resource] / _cby Jon Wakefield.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXIX, 697 p. 140 illus., 6 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Statistics, _x0172-7397
505	0		_aIntroduction -- Frequentist Inference -- Bayesian Inference -- Linear Models -- Binary Data Models -- General Regression Models.
520			_aBayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place.  The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines. While the philosophy behind each approach is discussed, the book is not ideological in nature and an emphasis is placed on practical application. It is shown that, in many situations, careful application of the respective approaches can lead to broadly similar conclusions. To use this text, the reader requires a basic understanding of calculus and linear algebra, and introductory courses in probability and statistical theory. The book is based on the author's experience teaching a graduate sequence in regression methods. The book website contains all of the code to reproduce all of the analyses and figures contained in the book.
650		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aStatistics, general.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781441909244
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4419-0925-1
912			_aZDB-2-SMA
999			_c94436 _d94436