| Titre : |
Computer age statistical inference : algorithms, evidence, and data science |
| Type de document : |
texte imprimé |
| Auteurs : |
Bradley Efron (1938-....), Auteur ; Trevor J. Hastie (1953-....), Auteur |
| Editeur : |
Cambridge ; New York ; Melbourne [UK ; USA] : Cambridge University Press (CUP) |
| Année de publication : |
2016, cop. 2016 |
| Collection : |
Institute of mathematical statistics monographs num. 5 |
| Importance : |
1 vol. (XIX-475 p.) |
| Présentation : |
ill. en noir et en coul. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-1-107-14989-2 |
| Note générale : |
Autre tirage : 2017 (5ème) .- PPN 199444625 |
| Langues : |
Anglais (eng) |
| Tags : |
Statistique mathématique -- Informatique Inférence Statistique bayésienne Distribution (théorie des probabilités) Familles exponentielles (statistique) Nonparametric statistics Bootstrap (statistique) Données massives Analyse de régression Ridge régression (statistique) Arbres de régression multivariable Mathematical statistics -- Data processing Inference Bayesian statistical decision theory Big data Distribution (Probability theory) Statistique non paramétrique Bootstrap (Statistics) Regression analysis Ridge regression (Statistics) Multivariate regression trees |
| Index. décimale : |
519.5 Statistique mathématique |
| Résumé : |
The 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 |
| Note de contenu : |
Sommaire (abrégé) : Preface (p. xv) - Acknowledgments (p.xviii) - Notation (p.xix) - Part I. Classical statistical inference (p.1) - Part II. Early-computer age methods (p.73) - Part III. Twenty-first-century topics (p.269) - Epilogue (p.446) - References (p. 453-462) - Author Index (p.463) - Subject Index (p.467) |
Computer age statistical inference : algorithms, evidence, and data science [texte imprimé] / Bradley Efron (1938-....), Auteur ; Trevor J. Hastie (1953-....), Auteur . - Cambridge ; New York ; Melbourne (UK ; USA) : Cambridge University Press (CUP), 2016, cop. 2016 . - 1 vol. (XIX-475 p.) : ill. en noir et en coul. ; 24 cm. - ( Institute of mathematical statistics monographs; 5) . ISBN : 978-1-107-14989-2 Autre tirage : 2017 (5ème) .- PPN 199444625 Langues : Anglais ( eng)
| Tags : |
Statistique mathématique -- Informatique Inférence Statistique bayésienne Distribution (théorie des probabilités) Familles exponentielles (statistique) Nonparametric statistics Bootstrap (statistique) Données massives Analyse de régression Ridge régression (statistique) Arbres de régression multivariable Mathematical statistics -- Data processing Inference Bayesian statistical decision theory Big data Distribution (Probability theory) Statistique non paramétrique Bootstrap (Statistics) Regression analysis Ridge regression (Statistics) Multivariate regression trees |
| Index. décimale : |
519.5 Statistique mathématique |
| Résumé : |
The 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 |
| Note de contenu : |
Sommaire (abrégé) : Preface (p. xv) - Acknowledgments (p.xviii) - Notation (p.xix) - Part I. Classical statistical inference (p.1) - Part II. Early-computer age methods (p.73) - Part III. Twenty-first-century topics (p.269) - Epilogue (p.446) - References (p. 453-462) - Author Index (p.463) - Subject Index (p.467) |
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