MARC details
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| fixed length control field |
03079nam a2200253 a 4500 |
| 001 - CONTROL NUMBER |
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42430 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
0000000000 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240411193048.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
210713n s 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
978-1107149892 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Efron, Bradley. |
| 245 10 - TITLE STATEMENT |
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| Title |
Computer age statistical inference : |
| Medium |
[electronic resource] |
| Remainder of title |
algorithms, evidence, and data science / |
| Statement of responsibility, etc. |
Bradley Efron; Trevor Hastie. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
New York, NY : |
| Name of publisher, distributor, etc. |
Cambridge University Press, |
| Date of publication, distribution, etc. |
2016. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource. |
| 490 1# - SERIES STATEMENT |
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| Series statement |
Institute of Mathematical Statistics monographs |
| Volume/sequential designation |
5 |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Part 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 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Mathematical statistics |
| General subdivision |
Data processing. |
| Source of heading or term |
sears |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Mathematics. |
| Source of heading or term |
sears |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hastie, Trevor |
| Relator term |
Author |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://drive.google.com/file/d/1IbnDcAvcGljJspDBvinWgH9bwiktZNh6/view?usp=sharing">https://drive.google.com/file/d/1IbnDcAvcGljJspDBvinWgH9bwiktZNh6/view?usp=sharing</a> |
