000			03079nam a2200253 a 4500
001			42430
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005			20240411193048.0
008			210713n s 000 0 eng d
020			_a978-1107149892
100	1		_aEfron, Bradley.
245	1	0	_aComputer age statistical inference : _h[electronic resource] _balgorithms, evidence, and data science / _cBradley Efron; Trevor Hastie.
260			_aNew York, NY : _bCambridge University Press, _c2016.
300			_a1 online resource.
490	1		_aInstitute of Mathematical Statistics monographs _v5
505	0		_aPart I. Classic Statistical Inference: -- Algorithms and inference -- Frequentist inference -- Bayesian inference -- Fisherian inference and maximum likelihood estimation -- Parametric models and exponential families -- Part II. Early Computer-Age Methods: -- Empirical Bayes -- James--Stein estimation and ridge regression -- Generalized linear models and regression trees -- Survival analysis and the EM algorithm -- The jackknife and the bootstrap -- Bootstrap confidence intervals -- Cross-validation and Cp estimates of prediction error -- Objective Bayes inference and Markov chain Monte Carlo -- Statistical inference and methodology in the postwar era -- Part III. Twenty-First Century Topics: -- Large-scale hypothesis testing and false discovery rates -- Sparse modeling and the lasso -- Random forests and boosting -- Neural networks and deep learning -- Support-vector machines and kernel methods -- Inference after model selection -- Empirical Bayes estimation strategies -- Epilogue.
520			_aThe twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science. -- Provided by publisher.
650		7	_aMathematical statistics _xData processing. _2sears
650		7	_aMathematics. _2sears
700	1		_aHastie, Trevor _eAuthor
856			_uhttps://drive.google.com/file/d/1IbnDcAvcGljJspDBvinWgH9bwiktZNh6/view?usp=sharing
999			_c12514 _d12514