000			01676cam a22002418i 4500
999			_c38188 _d38188
020			_a9781032209470
041			_aeng
082	0	0	_a300.15 _bFIN-A
100	1		_aFinch, W. Holmes _q(William Holmes), _eauthor.
245	1	0	_aApplied regularization methods for the social sciences / _cHolmes Finch.
250			_aFirst edition.
260			_aBoca Raton : _bCRC, _c2022.
300			_avii, 297p.
504			_aIncludes bibliographical references and index.
505	0		_aR -- Theoretical underpinnings of regularization methods -- Regularization methods for linear models -- Regularization methods for generalized linear models -- Regularization methods for multivariate linear models -- Regularization methods for cluster analysis and principal components analysis -- Regularization methods for latent variable models -- Regularization methods for multilevel models.
520			_a"Researchers in the social sciences are faced with complex data sets in which they have relatively small samples and many variables (high dimensional data). Unlike the various technical guides currently on the market, Applied Regularization Methods for the Social Sciences provides and overview of a variety of models alongside clear examples of hands-on application. Each chapter in this book covers a specific application of regularization techniques with a user-friendly technical description, followed by examples that provide a thorough demonstration of the methods in action"--
546			_aEnglish.
650		0	_aSocial sciences _xStatistical methods.
650		0	_aBig data.
650		0	_aMathematical statistics.
650		0	_aR (Computer program language)
942			_2ddc _cBK