000			04175nam a22004575i 4500
001			978-0-8176-4425-3
003			DE-He213
005			20251006084434.0
007			cr nn 008mamaa
008			100301s2005 xxu| s |||| 0|eng d
020			_a9780817644253
020			_a99780817644253
024	7		_a10.1007/b138864 _2doi
082	0	4	_a519.5 _223
100	1		_aSahai, Hardeo. _eauthor.
245	1	0	_aAnalysis of Variance for Random Models _h[electronic resource] : _bVolume II: Unbalanced Data Theory, Methods, Applications, and Data Analysis / _cby Hardeo Sahai, Mario Miguel Ojeda.
264		1	_aBoston, MA : _bBirkhäuser Boston, _c2005.
300			_aXXV, 480 p. 14 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aMatrix Preliminaries and General Linear Model -- Some General Methods for Making Inferences about Variance Components -- One-Way Classification -- Two-Way Crossed Classification without Interaction -- Two-Way Crossed Classification with Interaction -- Three-Way and Higher-Order Crossed Classifications -- Two-Way Nested Classification -- Three-Way Nested Classification -- General r-Way Nested Classification.
520			_aAnalysis of variance (ANOVA) models have become widely used tools and play a fundamental role in much of the application of statistics today. In particular, ANOVA models involving random effects have found widespread application to experimental design in a variety of fields requiring measurements of variance, including agriculture, biology, animal breeding, applied genetics, econometrics, quality control, medicine, engineering, and social sciences. This two-volume work is a comprehensive presentation of different methods and techniques for point estimation, interval estimation, and tests of hypotheses for linear models involving random effects. Both Bayesian and repeated sampling procedures are considered. Volume I examines models with balanced data (orthogonal models); Volume II studies models with unbalanced data (nonorthogonal models). Features and Topics: * Systematic treatment of the commonly employed crossed and nested classification models used in analysis of variance designs * Detailed and thorough discussion of certain random effects models not commonly found in texts at the introductory or intermediate level * Numerical examples to analyze data from a wide variety of disciplines * Many worked examples containing computer outputs from standard software packages such as SAS, SPSS, and BMDP for each numerical example * Extensive exercise sets at the end of each chapter * Numerous appendices with background reference concepts, terms, and results * Balanced coverage of theory, methods, and practical applications * Complete citations of important and related works at the end of each chapter, as well as an extensive general bibliography Accessible to readers with only a modest mathematical and statistical background, the work will appeal to a broad audience of students, researchers, and practitioners in the mathematical, life, social, and engineering sciences. It may be used as a textbook in upper-level undergraduate and graduate courses, or as a reference for readers interested in the use of random effects models for data analysis.
650		0	_aSTATISTICS.
650		0	_aDISTRIBUTION (PROBABILITY THEORY).
650		0	_aMATHEMATICAL STATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
650	2	4	_aPROBABILITY THEORY AND STOCHASTIC PROCESSES.
650	2	4	_aSTATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY & GEOSCIENCES.
650	2	4	_aSTATISTICS FOR LIFE SCIENCES, MEDICINE, HEALTH SCIENCES.
700	1		_aOjeda, Mario Miguel. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780817632298
856	4	0	_uhttp://dx.doi.org/10.1007/b138864 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c59626 _d59626