A comparison of bootstrap methods and an adjusted bootstrap ...
A comparison of bootstrap methods and an adjusted bootstrap ...
A comparison of bootstrap methods and an adjusted bootstrap ...
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ootstrap method <strong><strong>an</strong>d</strong> demonstrated that it is more robust th<strong>an</strong> other <strong>methods</strong> acrossvarying signal levels <strong><strong>an</strong>d</strong> classifiers in this context. For small to moderate sized samples,we suggest using the <strong>adjusted</strong> <strong>bootstrap</strong> method since 1) it remains conservative, henceavoids overly optimistic assessment <strong>of</strong> a prediction model; 2) it does not suffer fromextremely large bias or variability in <strong>comparison</strong> to other <strong>methods</strong>. These features <strong>of</strong> the<strong>adjusted</strong> <strong>bootstrap</strong> method are particularly appealing for small samples when otherprediction error estimation <strong>methods</strong> encounter difficulties.REFERENCES1. Stone M. Cross-validatory choice <strong><strong>an</strong>d</strong> assessment <strong>of</strong> statistical predictions. Journal <strong>of</strong>the Royal Statistical Society, Serial B 1974; 36: 111-147.2. Efron B. Estimating the error rate <strong>of</strong> a prediction rule: improvement on crossvalidation.Journal <strong>of</strong> the Americ<strong>an</strong> Statistical Association 1983; 78: 316-331.3. Efron B, Tibshir<strong>an</strong>i R. Improvement on cross-validation: the .632+ <strong>bootstrap</strong> method.Journal <strong>of</strong> the Americ<strong>an</strong> Statistical Association 1997; 92: 548-560.4. Efron B, Tibshir<strong>an</strong>i R. An Introduction to the Bootstrap. Chapm<strong>an</strong> <strong><strong>an</strong>d</strong> Hall, 1998.5. Breim<strong>an</strong> L. Out-<strong>of</strong> –bag estimation. Technical Report, Department <strong>of</strong> Statistics,University <strong>of</strong> California, Berkeley, 1996.6. Breim<strong>an</strong> L. Bagging predictors. Machine Learning 1996; 24:123-140.7. Dudoit S, Fridly<strong><strong>an</strong>d</strong> J. Classification in microarray experiments. Statistical Analysis <strong>of</strong>Gene Expression Microarray Data. Chapm<strong>an</strong> <strong><strong>an</strong>d</strong> Hall, 2003; pp 93-158.8. Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a <strong>comparison</strong> <strong>of</strong>resampling <strong>methods</strong>. Bioinformatics 2005; 21: 3301-3307.9. Fu W, Carroll RJ, W<strong>an</strong>g S. Estimating misclassification error with small samples via<strong>bootstrap</strong> cross-validation. Bioinformatics 2005; 21: 1979-1986.10. Simon R, Radmacher MD, Dobbin K, McSh<strong>an</strong>e LM. Pitfalls in the use <strong>of</strong> DNAmicroarray data for diagnostic <strong><strong>an</strong>d</strong> prognostic classification. Journal <strong>of</strong> the NationalC<strong>an</strong>cer Institute 2003; 95: 14-18.11. Ambroise C, McLachl<strong>an</strong> GJ. Selection bias in gene extraction on the basis <strong>of</strong>microarray gene-expression data. Proceedings <strong>of</strong> National Academy <strong>of</strong> Science 2002; 99:6562-6566.18
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