000			03745nam a22004695i 4500
001			978-1-4614-6363-4
003			DE-He213
005			20140220082824.0
007			cr nn 008mamaa
008			130410s2013 xxu| s |||| 0|eng d
020			_a9781461463634 _9978-1-4614-6363-4
024	7		_a10.1007/978-1-4614-6363-4 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMBNS _2bicssc
072		7	_aMED090000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aPronzato, Luc. _eauthor.
245	1	0	_aDesign of Experiments in Nonlinear Models _h[electronic resource] : _bAsymptotic Normality, Optimality Criteria and Small-Sample Properties / _cby Luc Pronzato, Andrej Pázman.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXV, 399 p. 56 illus., 37 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		_aLecture Notes in Statistics, _x0930-0325 ; _v212
505	0		_aIntroduction -- Asymptotic designs and uniform convergence. Asymptotic properties of the LS estimator -- Asymptotic properties of M, ML and maximum a posteriori estimators -- Local optimality criteria based on asymptotic normality -- Criteria based on the small-sample precision of the LS estimator -- Identifiability, estimability and extended optimality criteria -- Nonlocal optimum design -- Algorithms—a survey -- Subdifferentials and subgradients -- Computation of derivatives through sensitivity functions -- Proofs -- Symbols and notation -- List of labeled assumptions -- References.
520			_aDesign of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensive coverage of the various aspects of experimental design for nonlinear models. The book contains original contributions to the theory of optimal experiments that will interest students and researchers in the field. Practitionners motivated by applications will find valuable tools to help them designing their experiments.  The first three chapters expose the connections between the asymptotic properties of estimators in parametric models and experimental design, with more emphasis than usual on some particular aspects like the estimation of a nonlinear function of the model parameters, models with heteroscedastic errors, etc. Classical optimality criteria based on those asymptotic properties are then presented thoroughly in a special chapter.  Three chapters are dedicated to specific issues raised by nonlinear models. The construction of design criteria derived from non-asymptotic considerations (small-sample situation) is detailed. The connection between design and identifiability/estimability issues is investigated. Several approaches are presented to face the problem caused by the dependence of an optimal design on the value of the parameters to be estimated.  A survey of algorithmic methods for the construction of optimal designs is provided.
650		0	_aStatistics.
650	1	4	_aStatistics.
650	2	4	_aStatistics for Life Sciences, Medicine, Health Sciences.
650	2	4	_aStatistics, general.
650	2	4	_aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
700	1		_aPázman, Andrej. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461463627
830		0	_aLecture Notes in Statistics, _x0930-0325 ; _v212
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-6363-4
912			_aZDB-2-SMA
999			_c95654 _d95654