com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap

Class BootstrapEstimator

java.lang.Object

com.numericalmethod.suanshu.stats.sampling.resampling.bootstrap.BootstrapEstimator

All Implemented Interfaces:

java.io.Serializable

public class BootstrapEstimator
extends java.lang.Object
implements java.io.Serializable

This class estimates the statistic for a sample using a bootstrap method.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
BootstrapEstimator(Resampling bootstrap, StatisticFactory factory, int B) Constructs a bootstrap estimator.
BootstrapEstimator(Resampling bootstrap, StatisticFactory factory, int B, boolean isParallel) Constructs a bootstrap estimator.

Method Summary

Modifier and Type	Method and Description
double	value() The estimator value.
double	variance() The estimator variance, of which the convergence limit is decided by sample size, not B.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

BootstrapEstimator

public BootstrapEstimator(Resampling bootstrap,
                          StatisticFactory factory,
                          int B)

Constructs a bootstrap estimator.

Parameters:

bootstrap - the bootstrap method and the sample

factory - the statistic

B - the number of bootstrap replicas

BootstrapEstimator

public BootstrapEstimator(Resampling bootstrap,
                          StatisticFactory factory,
                          int B,
                          boolean isParallel)

Constructs a bootstrap estimator.

Parameters:

bootstrap - the bootstrap method and the sample

factory - the statistic

B - the number of bootstrap replicas

isParallel - true if to run in parallel cores

Method Detail

value

public double value()

The estimator value.

Returns:

the estimator value

variance

public double variance()

The estimator variance, of which the convergence limit is decided by sample size, not B.

Returns:

the estimator variance