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003			OSt
005			20251231143709.0
008			230218s2023 flu b 001 0 eng
010			_a 2022052687
020			_a9780367763442 (hbk.)
035			_a22980286
040			_aLBSOR _beng _erda _cJKRC _dDLC
082	0	0	_a006.310727 _223 _bAHM.S
100	1		_aAhmed, Syed Ejaz. _eauthor. _938228
245	1	0	_aPost-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data / _cby Syed Ejaz Ahmed, Feryaal Ahmed, and Bahadir Yuzbasi. _hEnglish.
250			_a1st.
260			_aBoca Raton : _bCRC Press Taylor & Francis Group, _c2023.
263			_a2312
300			_axxix, 378 p. ; _c26 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
365			_b125.00 _cPound
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction to machine learning -- Post shrinkage strategies in sparse regression models -- Shrinkage strategies in high-dimensional regression models -- Shrinkage estimation strategies in partially linear models -- Shrinkage strategies : generalized linear models -- Post shrinkage strategy in sparse linear mixed models -- Shrinkage estimation in sparse nonlinear regression models -- Shrinkage strategies in sparse robust regression models -- Liu-type shrinkage estimations in linear sparse models.
520			_a"This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in data science. It combines statistical learning and machine learning techniques in a unique and optimal way. It is well-known that machine learning methods are subject to many issues relating to bias, and consequently the mean squared error and prediction error may explode. For this reason, we suggest shrinkage strategies to control the bias by combining a submodel selected by a penalized method with a model with many features. Further, the suggested shrinkage methodology can be successfully implemented for high dimensional data analysis. Many researchers in statistics and medical sciences work with big data. They need to analyse this data through statistical modelling. Estimating the model parameters accurately is an important part of the data analysis. This book may be a repository for developing improve estimation strategies for statisticians. This book will help researchers and practitioners for their teaching and advanced research, and is an excellent textbook for advanced undergraduate and graduate courses involving shrinkage, statistical, and machine learning"-- _cProvided by publisher.
650		0	_aMachine learning _xStatistical methods. _938229
650		0	_aPredictive analytics. _938230
650		0	_aRegression analysis _xMathematical models. _938231
650		0	_aEstimation theory. _934123
650		0	_aQuantitative research _xData processing. _933685
700	1		_aAhmed, Feryaal. _eauthor. _938232
700	1		_aYuzbasi, Bahadir. _eauthor. _938233
776	0	8	_iOnline version: _aAhmed, S. E. (Syed Ejaz), 1957- _tShrinkage strategies in analytics and data science _dBoca Raton : CRC Press, 2023 _z9781003170259 _w(DLC) 2022052688
906			_a7 _brip _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _c1 _e23 _n0
999			_c613170 _d613170