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The 90% confidence ellipse assuming bivariate normality and 90% nonparametric confidence region based on 5000 bootstrap samples are shown in Figure 3.

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The results of the final regression models, which incorporated variables found to be significant in 60% of the bootstrap sample are shown in Table  4.

The 95% confidence interval of bootstrapping and FDR of permutation re-sampling are shown in Figure 4 and Table 2. To determine temporal dynamics and meaningful biological functions, only HMM-specific TDE genes which are not contained in static methods are further explored in gene clustering and biological functional network analysis as shown in Figures 5(a) and 5(b), respectively.

The corresponding 95% percentile intervals (based on 1000 bootstrap samples) are also shown.

The sensitivity analysis model shows that with an expected threshold of 53,357.1 -FF96.396.3% of the bootstrap samples are cost-effective.

The wild bootstrap samples are constructed by adding the bootstrap residuals : (16).

The bootstrap samples are twice the number of measurements.

(5) The wild bootstrap samples are constructed by adding the bootstrap residuals : (16) We use to denote a wild bootstrap dataset.

Regression coefficients in bootstrap samples were often non-identifiable (results not shown), resulting in unstable estimates and over-coverage of multivariable regression coefficients.

Also, a variant of the CPF approach based on the bootstrap sampling (BS) is shown to exhibit good performance in the presence of reduced number of observations.

Each of the bootstrap samples is considered as new data.

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