000			03843nam a22005655i 4500
001			978-3-319-68253-2
003			DE-He213
005			20210118125449.0
007			cr nn 008mamaa
008			171128s2017 gw | s |||| 0|eng d
020			_a9783319682532 _9978-3-319-68253-2
024	7		_a10.1007/978-3-319-68253-2 _2doi
050		4	_aQA273.A1-274.9
050		4	_aQA274-274.9
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
072		7	_aPBT _2thema
072		7	_aPBWL _2thema
082	0	4	_a519.2 _223
100	1		_aJ. Olive, David. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aRobust Multivariate Analysis _h[electronic resource] / _cby David J. Olive.
250			_a1st ed. 2017.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017.
300			_aXVI, 501 p. 76 illus., 6 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Multivariate Distributions -- Elliptically Contoured Distributions -- MLD Estimators -- DD Plots and Prediction Regions -- Principal Component Analysis -- Canonical Correlation Analysis -- Discrimination Analysis -- Hotelling's T^2 Test -- MANOVA -- Factor Analysis -- Multivariate Linear Regression -- Clustering -- Other Techniques -- Stuff for Students.
520			_aThis text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given. The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis. A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with outliers. Many R programs and R data sets are available on the author’s website. .
650		0	_aProbabilities.
650		0	_aStatistics .
650	1	4	_aProbability Theory and Stochastic Processes. _0http://scigraph.springernature.com/things/product-market-codes/M27004
650	2	4	_aStatistical Theory and Methods. _0http://scigraph.springernature.com/things/product-market-codes/S11001
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319682518
776	0	8	_iPrinted edition: _z9783319682525
776	0	8	_iPrinted edition: _z9783319885711
856	4	0	_uhttps://doi.org/10.1007/978-3-319-68253-2
912			_aZDB-2-SMA
999			_c446984 _d446984
942			_cEB
506			_aAvailable to subscribing member institutions only. Доступно лише організаціям членам підписки.
506			_fOnline access from local network of NaUOA.
506			_fOnline access with authorization at https://link.springer.com/
506			_fОнлайн-доступ з локальної мережі НаУОА.
506			_fОнлайн доступ з авторизацією на https://link.springer.com/