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Analyzing receiver operating characteristic curves with SAS /
As a diagnostic decision-making tool, receiver operating characteristic (ROC) curves provide a comprehensive and visually attractive way to summarize the accuracy of predictions. They are used extensively in medical diagnosis and increasingly in fields such as data mining, credit scoring, weather fo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cary, NC :
SAS Institute,
2007.
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| Colección: | SAS Press series.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Analyzing receiver operating characteristic curves with SAS / |c Mithat Gonen. |
| 246 | 3 | |a Analyzing ROC curves with SAS | |
| 260 | |a Cary, NC : |b SAS Institute, |c 2007. | ||
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| 504 | |a Includes bibliographical references (pages 127-128) and index. | ||
| 505 | 0 | |a Introduction -- Single binary predictor -- Single continuous predictor -- Comparison and covariate adjustment of ROC curves -- Ordinal predictors -- Lehmann family of ROC curves -- ROC curves with censored data -- Using the ROC curve to evaluate multivariable prediction models -- ROC curves in SAS Enterprise Miner. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a As a diagnostic decision-making tool, receiver operating characteristic (ROC) curves provide a comprehensive and visually attractive way to summarize the accuracy of predictions. They are used extensively in medical diagnosis and increasingly in fields such as data mining, credit scoring, weather forecasting, and psychometry. In Analyzing Receiver Operating Characteristic Curves with SAS, author Mithat Gonen illustrates the many existing SAS procedures that can be tailored to produce ROC curves and expands upon further analyses using other SAS procedures and macros. Both parametric and nonparametric methods for analyzing ROC curves are covered in detail. Topics addressed include: Appropriate methods for binary, ordinal, and continuous measures Computations using PROC FREQ, PROC LOGISTIC, PROC NLMIXED, and macros Comparing the ROC curves of several markers and adjusting them for covariates ROC curves with censored data Using the ROC curve for evaluating multivariable prediction models via bootstrap and cross-validation ROC curves in SAS Enterprise Miner And more! Written for any statistician interested in learning more about ROC curve methodology, the book assumes readers have a basic understanding of regression procedures and moderate familiarity with Base SAS and SAS/STAT. Some familiarity with SAS/GRAPH is helpful but not essential. This book is part of the SAS Press program. | ||
| 542 | |f Copyright: SAS Institute |g 2009 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 600 | 1 | 0 | |a Lehmann, E. L. |q (Erich Leo), |d 1917-2009. |
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