Cross-Validated Bagged Prediction of Survival

Корнилова Людмила Владимировна · Statistical Applications in Genetics and Molecular Biology · 2006

Summary

This article shows how to apply the previously proposed Deletion/Substitution/Addition algorithm in the context of right-censoring for the prediction of survival and introduces how to incorporate bagging into the algorithm to obtain a cross-validated bagged estimator.

Type: article
Published: 2006-01-01
Cited by: 8
References: 34
DOI: https://doi.org/10.2202/1544-6115.1180
PubMed: https://pubmed.ncbi.nlm.nih.gov/16676466/
OpenAlex: https://openalex.org/W16676466
Semantic Scholar: https://api.semanticscholar.org/CorpusID:41046313

Keywords

Modernization theory, Competence (human resources), Perfection, Pedagogy, Psychology

References

Tree-structured survival analysis.Louisa G. Gordon, Richard A. Olshen — 1985
Using Iterated Bagging to Debias RegressionsLeo Breiman — 2001
Optimization of the Architecture of Neural Networks Using a Deletion/Substitution/Addition AlgorithmBlythe Durbin, Sandrine Dudoit, Mark J. van der Laan — 2005
Unified Methods for Censored Longitudinal Data and CausalityMark J. van der Laan, James M. Robins — 2003
The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.Andreas Rosenwald, George W. Wright, Wing C. Chan — 2002
Exponential survival trees.Roger B. Davis, James R. Anderson — 1989
Regression Trees for Censored DataMark R. Segal — 1988
Combining Estimates in Regression and ClassificationMichael LeBlanc, Robert Tibshirani — 1996
Dimension reduction strategies for analyzing global gene expression data with a response.Francesca Chiaromonte, Jessica Martinelli — 2002
Bundling classifiers by bagging treesTorsten Hothorn, Berthold Lausen — 2005
Signatures of the immune response.Arthur L. Shaffer, Andreas Rosenwald, Elaine M. Hurt — 2001
Deletion/Substitution/Addition Algorithm in Learning with Applications in GenomicsSandra E. Sinisi, Mark J. van der Laan — 2004
Tree-based multivariate regression and density estimation with right-censored dataAnnette M. Molinaro, Sandrine Dudoit, Mark J. van der Laan — 2004
Semi-Supervised Methods to Predict Patient Survival from Gene Expression DataEric Bair, Robert Tibshirani — 2004
Asymptotically optimal model selection method with right censored outcomesSündüz Keleş, Mark van der Laan, Sandrine Dudoit — 2004
Bagging for linear classifiersMarina Skurichina, Robert P. W. Duin — 1998
Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learningMargaret A. Shipp, Ken N. Ross, Pablo Tamayo — 2002
On bagging and nonlinear estimationJerome H. Friedman, Peter Hall — 2007
Cross-Validated Bagged Learning.Maya Petersen, Annette M. Molinaro, Sandra E. Sinisi — 2007
Heuristics of instability and stabilization in model selectionLeo Breiman — 1996

Cited by

The Association Between Exposure to Traffic-Related Air Pollution During Pregnancy and Children's Health Outcomes in the San Joaquin Valley of California: An Example of Causal Inference MethodsAmy Padula — 2010
Majorization-Minimization algorithms for nonsmoothly penalized objective functionsElizabeth D. Schifano, Robert L. Strawderman, Martin T. Wells — 2010
ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUEIan Gordon, Peter Hall — 2009
Evaluating Prediction Rules for t-Year Survivors With Censored Regression ModelsHajime Uno, Tianxi Cai, Lü Tian — 2007
Flexible Regression Model Selection for Survival Probabilities: With Application to AIDSA. Gregory DiRienzo — 2009
Reducing variability of crossvalidation for smoothing-parameter choiceP. Hall, Andrew P. Robinson — 2009
Topics In Penalized EstimationElizabeth D. Schifano — 2010

Related papers

No related papers recorded.