000			03908cam a22005297i 4500
001			9781003306979
003			FlBoTFG
005			20240213122827.0
006			m o d
007			cr cnu|||unuuu
008			220718s2023 flu o 000 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781003306979 _q(electronic bk.)
020			_a1003306977 _q(electronic bk.)
020			_a9781000624076 _q(electronic bk. : EPUB)
020			_a1000624072 _q(electronic bk. : EPUB)
020			_z9781032308487
020			_a9781000624007 _q(electronic bk. : PDF)
020			_a1000624005 _q(electronic bk. : PDF)
020			_z9780367030377
024	7		_a10.1201/9781003306979 _2doi
035			_a(OCoLC)1336501375
035			_a(OCoLC-P)1336501375
050		4	_aR853.S7
072		7	_aCOM _x037000 _2bisacsh
072		7	_aMAT _x029000 _2bisacsh
072		7	_aPBT _2bicssc
082	0	4	_a610.72/4 _223/eng/20220513
100	1		_aVaman, H. J., _eauthor.
245	1	0	_aSurvival analysis / _cH.J. Vaman, Prabhanjan Narayanachar Tattar.
250			_aFirst edition.
264		1	_aBoca Raton, FL : _bCRC Press, _c2023.
300			_a1 online resource (xii, 284 pages).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
505	0		_aI Classical Survival Analysis. 1. Lifetime Data and Concepts. 2 Core Concepts. 3. Inference - Estimation. 4. Inference - Statistical Tests. 5. Regression Models. 6. Further Topics in Regression Models. 7. Model Selection.II Machine Learning Methods. Why Machine Learning? 8. Survival Trees. 9. Ensemble Survival Analysis. 10. Neural Network Survival Analysis. 11. Complementary Machine Learning Techniques. Bibliography. Index.
520			_aSurvival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model, Aalen's additive hazards model, etc. Information criteria to facilitate model selection including Akaike, Bayes, and Focused Penalized methods Survival trees and ensemble techniques of bagging, boosting, and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book
588			_aOCLC-licensed vendor bibliographic record.
650		7	_aCOMPUTERS / Machine Theory _2bisacsh
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
650		0	_aSurvival analysis (Biometry)
650		0	_aClinical trials _xStatistical methods.
700	1		_aTattar, Prabhanjan, _d1979- _eauthor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003306979
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c5247 _d5247