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Packages that use Bootstrap.Estimate | |
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bb.science | Provides classes and interfaces for mathematical and scientific programming. |
Uses of Bootstrap.Estimate in bb.science |
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Fields in bb.science with type parameters of type Bootstrap.Estimate | |
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private ConcurrentHashMap<Bootstrap.Estimator,Bootstrap.Estimate> |
Bootstrap.estimatorToEstimate
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Methods in bb.science that return Bootstrap.Estimate | |
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Bootstrap.Estimate |
Bootstrap.getEstimate(Bootstrap.Estimator estimator)
Returns the Bootstrap.Estimate which corresponds to estimator. |
Bootstrap.Estimate |
Bootstrap.getEstimate(String estimatorName)
Returns the first Bootstrap.Estimate which corresponds to estimator with the same name as estimatorName. |
private Bootstrap.Estimate |
Bootstrap.UnitTest.CiTask.getEstimateTheory(Bootstrap.Estimator estimator,
double[] sample)
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Bootstrap.Estimate |
Bootstrap.UnitTest.Distribution.getMeanEst(double[] sample,
double confidenceLevel)
Returns a theoretically known Estimate for the mean given sample. |
Bootstrap.Estimate |
Bootstrap.UnitTest.GaussianStandard.getMeanEst(double[] sample,
double confidenceLevel)
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Bootstrap.Estimate |
Bootstrap.UnitTest.CauchyStandard.getMeanEst(double[] sample,
double confidenceLevel)
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Bootstrap.Estimate |
Bootstrap.UnitTest.ExponentialStandard.getMeanEst(double[] sample,
double confidenceLevel)
See this discussion (the solution is in the middle of the page; the original solution is claimed to be found in Kapur, K. |
Bootstrap.Estimate |
Bootstrap.UnitTest.Distribution.getMedianEst(double[] sample,
double confidenceLevel)
Returns a theoretically known Estimate for the median of this sample. |
Bootstrap.Estimate |
Bootstrap.UnitTest.DistributionAbstract.getMedianEst(double[] sample,
double confidenceLevel)
Implements an amazing theoretical result for the median confidence interval which is valid for any distribution, and only assumes iid for the samples. |
Bootstrap.Estimate |
Bootstrap.UnitTest.Distribution.getSdEst(double[] sample,
double confidenceLevel)
Returns a theoretically known Estimate for the sd of this sample. |
Bootstrap.Estimate |
Bootstrap.UnitTest.GaussianStandard.getSdEst(double[] sample,
double confidenceLevel)
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Bootstrap.Estimate |
Bootstrap.UnitTest.CauchyStandard.getSdEst(double[] sample,
double confidenceLevel)
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Bootstrap.Estimate |
Bootstrap.UnitTest.ExponentialStandard.getSdEst(double[] sample,
double confidenceLevel)
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Methods in bb.science that return types with arguments of type Bootstrap.Estimate | |
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private ConcurrentHashMap<Bootstrap.Estimator,Bootstrap.Estimate> |
Bootstrap.calcEstimates_BCa(Bootstrap.Estimator[] estimators)
Performs a bootstrap calculation, determining one Bootstrap.Estimate for each element of estimators. |
private ConcurrentHashMap<Bootstrap.Estimator,Bootstrap.Estimate> |
Bootstrap.calcEstimates_percentile(Bootstrap.Estimator[] estimators)
Performs a bootstrap calculation, determining one Bootstrap.Estimate for each element of estimators. |
private ConcurrentHashMap<Bootstrap.Estimator,Bootstrap.Estimate> |
Bootstrap.calcEstimates(Bootstrap.Estimator[] estimators)
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Methods in bb.science with parameters of type Bootstrap.Estimate | |
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private void |
Bootstrap.UnitTest.CiResult.Metrics.process(Bootstrap.Estimate estBs,
Bootstrap.Estimate estTheory)
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private void |
Bootstrap.UnitTest.CoverageResult.Metrics.process(Bootstrap.Estimate estimate,
double valueTrue)
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Method parameters in bb.science with type arguments of type Bootstrap.Estimate | |
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private void |
Bootstrap.UnitTest.CiResult.include(Bootstrap.UnitTest.CiTask task,
Map<Bootstrap.Estimator,Bootstrap.Estimate> resultsBs,
Map<Bootstrap.Estimator,Bootstrap.Estimate> resultsTheory)
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private void |
Bootstrap.UnitTest.CiResult.include(Bootstrap.UnitTest.CiTask task,
Map<Bootstrap.Estimator,Bootstrap.Estimate> resultsBs,
Map<Bootstrap.Estimator,Bootstrap.Estimate> resultsTheory)
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private void |
Bootstrap.UnitTest.CoverageResult.include(Bootstrap.UnitTest.CoverageTask task,
Map<Bootstrap.Estimator,Bootstrap.Estimate> estimatorToEstimate)
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