000			02968cam a2200349 i 4500
001			4755
003			BD-DhEWU
005			20181108115414.0
008			160616s2014 flua g b 001 0 eng d
010			_a 2014013362
020			_a9781466567283 (hardback)
020			_a1466567287
035			_a(OCLC)878953459
040			_aDLC _beng _cDLC _erda _dDLC _dBD-DhEWU
041			_aeng
050	0	0	_aQA278 _b.K597 2014
082	0	0	_a519.535 _223 _bKOI 2014
100	1		_aKonishi, Sadanori. _920294
245	1	0	_aIntroduction to multivariate analysis : _blinear and nonlinear modeling / _cSadanori Konishi.
260			_aBoca Raton : _bCRC Press ; _bTaylor & Francis Group, _c2014.
300			_axxv, 312 p. : _billus. ; _c24 cm.
490	0		_aChapman & Hall/CRC Texts in Statistical Science series.
504			_aIncludes bibliographical references (pages 299-307) and index.
505			_tTOC _aIntroduction Regression Modeling Classification and Discrimination Dimension Reduction Clustering Linear Regression Models Relationship between Two Variables Relationships Involving Multiple Variables Regularization Nonlinear Regression Models Modeling Phenomena Modeling by Basis Functions Basis Expansions Regularization Logistic Regression Models Risk Prediction Models Multiple Risk Factor Models Nonlinear Logistic Regression Models Model Evaluation and Selection Criteria Based on Prediction Errors Information Criteria Bayesian Model Evaluation Criterion Discriminant Analysis Fisher's Linear Discriminant Analysis Classification Based on Mahalanobis Distance Variable Selection Canonical Discriminant Analysis Bayesian Classification Bayes' Theorem Classification with Gaussian Distributions Logistic Regression for Classification Support Vector Machines Separating Hyperplane Linearly Nonseparable Case From Linear to Nonlinear Principal Component Analysis Principal Components Image Compression and Decompression Singular Value Decomposition Kernel Principal Component Analysis Clustering Hierarchical Clustering Nonhierarchical Clustering Mixture Models for Clustering Appendix A: Bootstrap Methods Appendix B: Lagrange Multipliers Appendix C: EM Algorithm Bibliography Index
520			_a"Multivariate techniques are used to analyze data that arise from more than one variable in which there are relationships between the variables. Mainly based on the linearity of observed variables, these techniques are useful for extracting information and patterns from multivariate data as well as for the understanding the structure of random phenomena. This book describes the concepts of linear and nonlinear
526			_aAS
590			_aSagar Shahanawaz
650		0	_aMultivariate analysis. _920295
856	4	2	_3WorldCat details _uhttp://www.worldcat.org/title/introduction-to-multivariate-analysis-linear-and-nonlinear-modeling/oclc/878953459&referer=brief_results
856	4	0	_3E-book Fulltext _uhttp://lib.ewubd.edu/ebook/4755
942			_2ddc _cTEXT _02
999			_c4755 _d4755