000			03418nam a22005535i 4500
001			978-3-642-16218-3
003			DE-He213
005			20140220083748.0
007			cr nn 008mamaa
008			110103s2011 gw | s |||| 0|eng d
020			_a9783642162183 _9978-3-642-16218-3
024	7		_a10.1007/978-3-642-16218-3 _2doi
050		4	_aQA276-280
072		7	_aUFM _2bicssc
072		7	_aCOM077000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aBaragona, Roberto. _eauthor.
245	1	0	_aEvolutionary Statistical Procedures _h[electronic resource] : _bAn Evolutionary Computation Approach to Statistical Procedures Designs and Applications / _cby Roberto Baragona, Francesco Battaglia, Irene Poli.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXII, 276 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStatistics and Computing, _x1431-8784
505	0		_aIntroduction -- Evolutionary Computation -- Evolving Regression Models -- Time Series Linear and Nonlinear Models -- Design of Experiments -- Outliers -- Cluster Analysis.
520			_aThis proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.
650		0	_aStatistics.
650		0	_aMedical laboratories.
650		0	_aComputer vision.
650		0	_aAlgorithms.
650		0	_aMathematical statistics.
650		0	_aSocial sciences _xMethodology.
650	1	4	_aStatistics.
650	2	4	_aStatistics and Computing/Statistics Programs.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aAlgorithms.
650	2	4	_aLaboratory Medicine.
650	2	4	_aMethodology of the Social Sciences.
700	1		_aBattaglia, Francesco. _eauthor.
700	1		_aPoli, Irene. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642162176
830		0	_aStatistics and Computing, _x1431-8784
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-16218-3
912			_aZDB-2-SMA
999			_c107132 _d107132