Sample.java
package org.drip.validation.evidence;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2020 Lakshmi Krishnamurthy
* Copyright (C) 2019 Lakshmi Krishnamurthy
*
* This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
* asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
* analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
* equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
* numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
* and computational support.
*
* https://lakshmidrip.github.io/DROP/
*
* DROP is composed of three modules:
*
* - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
* - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
* - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
*
* DROP Product Core implements libraries for the following:
* - Fixed Income Analytics
* - Loan Analytics
* - Transaction Cost Analytics
*
* DROP Portfolio Core implements libraries for the following:
* - Asset Allocation Analytics
* - Asset Liability Management Analytics
* - Capital Estimation Analytics
* - Exposure Analytics
* - Margin Analytics
* - XVA Analytics
*
* DROP Computational Core implements libraries for the following:
* - Algorithm Support
* - Computation Support
* - Function Analysis
* - Model Validation
* - Numerical Analysis
* - Numerical Optimizer
* - Spline Builder
* - Statistical Learning
*
* Documentation for DROP is Spread Over:
*
* - Main => https://lakshmidrip.github.io/DROP/
* - Wiki => https://github.com/lakshmiDRIP/DROP/wiki
* - GitHub => https://github.com/lakshmiDRIP/DROP
* - Repo Layout Taxonomy => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
* - Javadoc => https://lakshmidrip.github.io/DROP/Javadoc/index.html
* - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
* - Release Versions => https://lakshmidrip.github.io/DROP/version.html
* - Community Credits => https://lakshmidrip.github.io/DROP/credits.html
* - Issues Catalog => https://github.com/lakshmiDRIP/DROP/issues
* - JUnit => https://lakshmidrip.github.io/DROP/junit/index.html
* - Jacoco => https://lakshmidrip.github.io/DROP/jacoco/index.html
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
*
* You may obtain a copy of the License at
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/**
* <i>Sample</i> holds the Sample of Realizations.
*
* <br><br>
* <ul>
* <li>
* Bhattacharya, B., and D. Habtzghi (2002): Median of the p-value under the Alternate Hypothesis
* <i>American Statistician</i> <b>56 (3)</b> 202-206
* </li>
* <li>
* Head, M. L., L. Holman, R, Lanfear, A. T. Kahn, and M. D. Jennions (2015): The Extent and
* <i>Consequences of p-Hacking in Science PLoS Biology</i> <b>13 (3)</b> e1002106
* </li>
* <li>
* Wasserstein, R. L., and N. A. Lazar (2016): The ASA’s Statement on p-values: Context, Process,
* and Purpose <i>American Statistician</i> <b>70 (2)</b> 129-133
* </li>
* <li>
* Wetzels, R., D. Matzke, M. D. Lee, J. N. Rouder, G, J, Iverson, and E. J. Wagenmakers (2011):
* Statistical Evidence in Experimental Psychology: An Empirical Comparison using 855 t-Tests
* <i>Perspectives in Psychological Science</i> <b>6 (3)</b> 291-298
* </li>
* <li>
* Wikipedia (2019): p-value https://en.wikipedia.org/wiki/P-value
* </li>
* </ul>
*
* <br><br>
* <ul>
* <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
* <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ModelValidationAnalyticsLibrary.md">Model Validation Analytics Library</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/validation/README.md">Risk Factor and Hypothesis Validation, Evidence Processing, and Model Testing</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/validation/evidence/README.md">Sample and Ensemble Evidence Processors</a></li>
* </ul>
* <br><br>
*
* @author Lakshmi Krishnamurthy
*/
public class Sample implements org.drip.validation.evidence.NativePITGenerator
{
private double[] _realizationArray = null;
/**
* Sample Constructor
*
* @param realizationArray The Sample Realization Array
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public Sample (
final double[] realizationArray)
throws java.lang.Exception
{
if (null == (_realizationArray = realizationArray) ||
0 == _realizationArray.length ||
!org.drip.numerical.common.NumberUtil.IsValid (_realizationArray))
{
throw new java.lang.Exception ("Sample Constructor => Invalid Inputs");
}
}
/**
* Retrieve the Realization Array
*
* @return The Realization Array
*/
public double[] realizationArray()
{
return _realizationArray;
}
/**
* Apply the specified Test Statistic Evaluator to the Sample
*
* @param testStatisticEvaluator The Test Statistic Evaluator
*
* @return The Sample Test Statistic
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public double applyTestStatistic (
final org.drip.validation.evidence.TestStatisticEvaluator testStatisticEvaluator)
throws java.lang.Exception
{
if (null == testStatisticEvaluator)
{
throw new java.lang.Exception ("Sample::applyTestStatistic => Invalid Inputs");
}
return testStatisticEvaluator.evaluate (_realizationArray);
}
@Override public org.drip.validation.hypothesis.ProbabilityIntegralTransform
nativeProbabilityIntegralTransform()
{
org.drip.validation.evidence.TestStatisticAccumulator testStatisticAccumulator = new
org.drip.validation.evidence.TestStatisticAccumulator();
for (double realization : _realizationArray)
{
if (!testStatisticAccumulator.addTestStatistic (realization))
{
return null;
}
}
return testStatisticAccumulator.probabilityIntegralTransform();
}
}