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Physical Sciences and Mathematics Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Estimating Time-To-Event From Longitudinal Categorical Data Using Random Effects Markov Models: Application To Multiple Sclerosis Progression, Micha Mandel, Rebecca A. Betensky
Estimating Time-To-Event From Longitudinal Categorical Data Using Random Effects Markov Models: Application To Multiple Sclerosis Progression, Micha Mandel, Rebecca A. Betensky
Harvard University Biostatistics Working Paper Series
No abstract provided.
Evaluating The Roc Performance Of Markers For Future Events, Margaret Pepe, Yingye Zheng, Yuying Jin
Evaluating The Roc Performance Of Markers For Future Events, Margaret Pepe, Yingye Zheng, Yuying Jin
UW Biostatistics Working Paper Series
Receiver operating characteristic (ROC) curves play a central role in the evaluation of biomarkers and tests for disease diagnosis. Predictors for event time outcomes can also be evaluated with ROC curves, but the time lag between marker measurement and event time must be acknowledged. We discuss different definitions of time-dependent ROC curves in the context of real applications. Several approaches have been proposed for estimation. We contrast retrospective versus prospective methods in regards to assumptions and flexibility, including their capacities to incorporate censored data, competing risks and different sampling schemes. Applications to two datasets are presented.
Comment: Boosting Algorithms: Regularization, Prediction And Model Fitting, A. Buja, David Mease, A. Wyner
Comment: Boosting Algorithms: Regularization, Prediction And Model Fitting, A. Buja, David Mease, A. Wyner
David Mease
The authors are doing the readers of Statistical Science a true service with a well-written and up-to-date overview of boosting that originated with the seminal algorithms of Freund and Schapire. Equally, we are grateful for high-level software that will permit a larger readership to experiment with, or simply apply, boosting-inspired model fitting. The authors show us a world of methodology that illustrates how a fundamental innovation can penetrate every nook and cranny of statistical thinking and practice. They introduce the reader to one particular interpretation of boosting and then give a display of its potential with extensions from classification (where …
Comment: Boosting Algorithms: Regularization, Prediction And Model Fitting, A. Buja, David Mease, A. Wyner
Comment: Boosting Algorithms: Regularization, Prediction And Model Fitting, A. Buja, David Mease, A. Wyner
Faculty Publications
The authors are doing the readers of Statistical Science a true service with a well-written and up-to-date overview of boosting that originated with the seminal algorithms of Freund and Schapire. Equally, we are grateful for high-level software that will permit a larger readership to experiment with, or simply apply, boosting-inspired model fitting. The authors show us a world of methodology that illustrates how a fundamental innovation can penetrate every nook and cranny of statistical thinking and practice. They introduce the reader to one particular interpretation of boosting and then give a display of its potential with extensions from classification (where …