000			04295nam a22004695i 4500
001			978-0-387-28810-9
003			DE-He213
005			20250710083942.0
007			cr nn 008mamaa
008			100301s2005 xxu| s |||| 0|eng d
020			_a9780387288109 _a99780387288109
024	7		_a10.1007/0-387-28810-4 _2doi
082	0	4	_a519.5 _223
100	1		_aBrusco, Michael J. _eauthor.
245	1	0	_aBranch-and-Bound Applications in Combinatorial Data Analysis _h[recurso electrónico] / _cby Michael J. Brusco, Stephanie Stahl.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_aXII, 221 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStatistics and Computing, _x1431-8784
505	0		_aIntroduction -- An Introduction to Branch-and-Bound Methods for Partitioning -- Minimum-Diameter Partitioning -- Minimum Within-Cluster Sums of Dissimilarities Partitioning -- Minimum Within-Cluster Sums of Squares Partitioning -- Multiobjective Partitioning -- Introduction to the Branch-and-Bound Paradigm for Seriation -- Maximization of a Dominance Index -- Seriation--Maximization of Gradient Indices -- Seriation--Unidimensional Scaling -- Seriation--Multiobjective Seriation -- An Introduction to Branch-and-Bound Methods for Variable Selection -- Variable Selection for Cluster Analysis -- Variable Selection for Regression Analysis.
520			_aThere are a variety of combinatorial optimization problems that are relevant to the examination of statistical data. Combinatorial problems arise in the clustering of a collection of objects, the seriation (sequencing or ordering) of objects, and the selection of variables for subsequent multivariate statistical analysis such as regression. The options for choosing a solution strategy in combinatorial data analysis can be overwhelming. Because some problems are too large or intractable for an optimal solution strategy, many researchers develop an over-reliance on heuristic methods to solve all combinatorial problems. However, with increasingly accessible computer power and ever-improving methodologies, optimal solution strategies have gained popularity for their ability to reduce unnecessary uncertainty. In this monograph, optimality is attained for nontrivially sized problems via the branch-and-bound paradigm. For many combinatorial problems, branch-and-bound approaches have been proposed and/or developed. However, until now, there has not been a single resource in statistical data analysis to summarize and illustrate available methods for applying the branch-and-bound process. This monograph provides clear explanatory text, illustrative mathematics and algorithms, demonstrations of the iterative process, psuedocode, and well-developed examples for applications of the branch-and-bound paradigm to important problems in combinatorial data analysis. Supplementary material, such as computer programs, are provided on the world wide web. Dr. Brusco is a Professor of Marketing and Operations Research at Florida State University, an editorial board member for the Journal of Classification, and a member of the Board of Directors for the Classification Society of North America. Stephanie Stahl is an author and researcher with years of experience in writing, editing, and quantitative psychology research.
650		0	_aSTATISTICS.
650		0	_aOPERATIONS RESEARCH.
650		0	_aMATHEMATICAL STATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICS AND COMPUTING/STATISTICS PROGRAMS.
650	2	4	_aOPERATIONS RESEARCH/DECISION THEORY.
650	2	4	_aOPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING.
650	2	4	_aSTATISTICS FOR SOCIAL SCIENCE, BEHAVORIAL SCIENCE, EDUCATION, PUBLIC POLICY, AND LAW.
700	1		_aStahl, Stephanie. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387250373
830		0	_aStatistics and Computing, _x1431-8784
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-28810-4 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c56870 _d56870