Bootstrap
This class facilitates bootstrap sampling. The originalData is sampled from, with replacement, repeatedly to form bootstrap samples from which bootstrap statistics are computed. The estimator provides the mechanism for estimating statistical quantities from the original data. From the data, it can produce 1 or more estimated quantities. Bootstrap estimates are computed on the observed estimates from each bootstrap sample. The specified stream controls the bootstrap sampling process.
Constructors
Properties
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
Returns an 2-D array representation of the estimates from the bootstrapping process. The rows of the array are the multi-variate estimates from each bootstrap sample. The columns of the array represent the bootstrap estimates for each dimension across all the bootstrap samples.
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
If the save bootstrap data option was not turned on during the sampling then the list returned is empty.
Statistics collected across each dimension based on the estimates computed from each bootstrap sample. These statistics are cleared whenever generateSamples() is invoked in order to report statistics on the newly generated bootstrap samples.
Holds the estimated values (for each dimension) from the bootstrap samples. When the MVEstimator is applied to each bootstrap sample, it results in an array of estimates from the sample. This list holds those arrays. It is cleared whenever new samples are generated and then filled during the bootstrapping process.
If true, the stream will automatically participate in having its stream reset to its start stream via stream managers
Functions
Positions the RNG at the beginning of its next sub-stream
This method changes the underlying state of the Bootstrap instance by performing the bootstrap sampling.
Creates a random variable to represent the data in each bootstrap sample for which the data was saved.
Can be used by subclasses to implement logic that occurs within the boot sampling loop. The function is executed at the end of the main boot sampling loop. The parameter, estimate is the estimated quantities from the current bootstrap sample, bSample. For example, this function could be used to bootstrap on the bootstrap sample.
The returned list contains the bootstrap estimates for each of the dimensions. From these elements the bootstrap confidence intervals and other statistical analysis can be performed.
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