Bootstrap

This is the statistics of the bootstrap replicates.

If true, the stream will automatically participate in having its stream advanced to the next sub-stream via stream managers

Tells the stream to start producing antithetic variates

This is acrossBootstrapAverage - originalDataEstimate

Each element is the bootstrap estimate for sample i minus originalDataEstimate

These are the bootstrap replicates.

If the bootstrap samples were saved, this returns the generated averages for each of the samples

If the bootstrap samples were saved, this returns the generated variance for each of the samples

This is the standard deviation of the across bootstrap observations of the estimator for each bootstrap generated

If the save bootstrap data option was not turned on during the sampling then the list returned is empty.

the default confidence interval level

The number of bootstrap samples that were used to produce the bootstrap estimate

the average for the original data

The size of the original population of data from which to sample

If true, the stream will automatically participate in having its stream reset to its start stream via stream managers

the underlying stream of random numbers

Each element is the bootstrap estimate for sample i minus getOriginalDataEstimate() divided by getBootstrapStdErrEstimate()

The list itself is unmodifiable. The underlying statistic objects can be modified, but have no effect on the bootstrap generate statistics. The statistical values will be changed the next time generateSamples() is executed. Users are advised to copy the statistics in the list (via Statistic newInstance()) before executing generateSamples if persistence is required.

For the so called, BCa, interval, the approach requires an acceleration factor. The acceleration factor measures the rate of change of the standard error of the estimator with respect to the target parameter on a normalized scale. This function computes the acceleration factor based on the bootstrap estimates and the original estimated quantity using jackknifing.

Positions the RNG at the beginning of its next sub-stream

The "basic" method, but with no bias correction. This is the so-called centered percentile method (2θ − Bu , 2θ − Bl ) where θ is the bootstrap estimator and Bu is the 1 - alpha/2 percentile and Bl is the lower (alpha/2) percentile, where level = 1-alpha of the bootstrap replicates.

The BCa bootstrap confidence interval which accounts for bias correction and adjusted for acceleration.

For the so called, BCa, interval, the approach requires a bias correction factor which in essence measures the median bias of the bootstrap replicates for the estimated quantity. This function computes the bias correction factor based on the bootstrap estimates and the original estimated quantity.

This is the bootstrap-t or sometimes called percentile-t confidence interval. It is formed by capturing a t-type statistic which standardizes the individual bootstrap estimates. This confidence interval is only available if the parameter (numBootstrapTSamples) in the generateSamples() function is greater than 1; otherwise, the returned interval is (-infinity, +infinity).

Creates a random variable to represent the data in each bootstrap sample for which the data was saved.

This method changes the underlying state of the Bootstrap instance by performing the bootstrap sampling.

The "percentile" method, but with no bias correction. This is the percentile method (Bl , Bu ) where Bu is the 1 - alpha/2 percentile and Bl is the lower (alpha/2) percentile, where level = 1-alpha of the bootstrap replicates

The resetStartStream method will position the RNG at the beginning of its stream. This is the same location in the stream as assigned when the RNG was created and initialized.

Resets the position of the RNG at the start of the current sub-stream

Gets the standard normal based bootstrap confidence interval. Not recommended.

Assigns the stream associated with the supplied number from the default RNStreamProvider