000			03040nam a22004455i 4500
001			978-1-4614-5369-7
003			DE-He213
005			20140220082819.0
007			cr nn 008mamaa
008			130125s2013 xxu| s |||| 0|eng d
020			_a9781461453697 _9978-1-4614-5369-7
024	7		_a10.1007/978-1-4614-5369-7 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aGu, Chong. _eauthor.
245	1	0	_aSmoothing Spline ANOVA Models _h[electronic resource] / _cby Chong Gu.
250			_a2nd ed. 2013.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXVIII, 433 p. 82 illus., 69 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 ; _v297
505	0		_aIntroduction -- Model Construction -- Regression with Gaussian-Type Responses -- More Splines -- Regression and Exponential Families -- Regression with Correlated Responses -- Probability Density Estimation -- Hazard Rate Estimation -- Asymptotic Convergence -- Penalized Pseudo Likelihood.
520			_aNonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data analysis by practitioners. While scores of methods have proved successful for univariate smoothing, ones practical in multivariate settings number far less. Smoothing spline ANOVA models are a versatile family of smoothing methods derived through roughness penalties, that are suitable for both univariate and multivariate problems. In this book, the author presents a treatise on penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with censored life time data and covariates. The unifying themes are the general penalized likelihood method and the construction of multivariate models with built-in ANOVA decompositions. Extensive discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence.
650		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461453680
830		0	_aSpringer Series in Statistics, _x0172-7397 ; _v297
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-5369-7
912			_aZDB-2-SMA
999			_c95392 _d95392