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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BIASSTDMSECase 10.10.050-0.05-0.1-0.15-0.2-0.25-0.3-0.35OBSBCV0.632LOOCVOOBLOOBS0.632+ABS0.20.150.10.050OBSBCV0.632LOOCVOOBLOOBS0.632+ABS0.120.10.080.060.040.020OBSBCV0.632LOOCVOOBLOOBS0.632+ABSBIASSTDMSECase 20.020-0.02-0.04-0.06-0.08-0.1-0.12-0.14-0.16OBSBCV0.632LOOCVOOBLOOBS0.632+ABS0.120.10.080.060.040.020OBSBCV0.632LOOCVOOBLOOBS0.632+ABS0.0250.020.0150.010.0050OBSBCV0.632LOOCVOOBLOOBS0.632+ABSFigure 3. Comparison <strong>of</strong> Prediction Error Estimation on Simulated Datasets with n=40, p=800. Case 1: Nodifferentially expressed genes; Case 2: Class 0 patients follow normal distribution with me<strong>an</strong> 0, for class 1patients, the 2% differentially expressed genes follow normal with me<strong>an</strong> 1.5. Classification <strong><strong>an</strong>d</strong> regression tree isused in class prediction. The “true” prediction errors for the two cases are 0.500 <strong><strong>an</strong>d</strong> 0.221. Methods displayed areordinary <strong>bootstrap</strong> (OBS), <strong>bootstrap</strong> cross-validation (BCV), 632 <strong>bootstrap</strong>, leave-one-out cross-validation (LOOCV), out<strong>of</strong>-bagestimation (OOB), leave-one-out <strong>bootstrap</strong> (LOOBS),.632+ <strong>bootstrap</strong>, <strong><strong>an</strong>d</strong> <strong>adjusted</strong> <strong>bootstrap</strong> (ABS).
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