Bootstrap resampling

A) A distance matrix was constructed from the inferred amino acid sequences using a Poisson correction for multiple hits and the tree constructed using the minimum evolution approach. Five hundred bootstrap resamplings were carried out. Branches with bootstrap support values less than 50% are indicated with an asterisk. 

Resampling SeqBoot DNA/Protein Multiple data sets from bootstrap resampling... 

The double bootstrap was a method originally suggested by Efron (15) as a way to improve on the bootstrap bias correction of the apparent error rate of a linear discrimination rule. It is simply a bootstrap iteration (i.e., taking resamples from each bootstrap resample). The double bootstrap has been useful in improving the accuracy of confidence intervals but it substantially increases computation time and most likely increases the incidence of unsuccessfully terminated runs. It has been applied to linear models but not to PM modeling. 

Bootstrap resampling is performed twice within the PpbB approach to generate PK pseudoprofiles from which the function of interest is estimated. More specifically, the following scheme is adopted for the hith replicate at each time point ... 

Step 5. Perform B2 times bootstrap resampling with replacement from this empirical distribution P with sample size n each, where n is the average number of concentration replicates, and the corresponding parameter for each 62 = 1, , 2 is estimated. 

So for a sample with 8, 9, and 10 observations there are 6,435, 24,310, and 92,378 possible combinations, respectively. With 2000 bootstrap resamples and 20 observations, the probability is greater than 0.95 that no bootstrap sample will repeat (Chernick, 1999). As a practical rule, bootstrapping would not be advised with less than 10 observations, although Chernick (1999) suggests that no fewer than 50 be used. With eight or fewer observations an exact bootstrap estimate can be developed using all possible bootstrap combinations (Fisher and Hall, 1991). 

Bootstrap resampling provides two approaches nonparametric and parametric bootstrap samples. In both we begin with a mle or procedure fitted to n independent... 

Sensitivity and specificity were determined firom ROC curves to maximize the sum of the two values and constructed from the output of logistic regression analysis fit with either CEP adduct, CML, or pentosidine concentrations alone, or in combination. The concentration of CML was expressed in nmol/ml, and pentosidine and CEP adducts were expressed in pmol/ml. C-statistics, 95% Cl and p values derived from single and joint markers were determined with SAS 9.1 based on log-transformed maker concentrations. Verification of c-statistics and 95% Cl was performed by bootstrap resampling and 10-fold cross-validation. Pentosidine plus CEP appears to be a better discriminator than CEP alone (p -0.02) and CML plus pentosidine may be a better discriminator than CEP alone (p 0.06). 

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