TTestOutcome.java

  1. package org.drip.validation.hypothesis;

  3. /*
  4.  * -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
  5.  */

  7. /*!
  8.  * Copyright (C) 2020 Lakshmi Krishnamurthy
  9.  * Copyright (C) 2019 Lakshmi Krishnamurthy
  10.  * 
  11.  *  This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
  12.  *      asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
  13.  *      analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
  14.  *      equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
  15.  *      numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
  16.  *      and computational support.
  17.  *  
  18.  *      https://lakshmidrip.github.io/DROP/
  19.  *  
  20.  *  DROP is composed of three modules:
  21.  *  
  22.  *  - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
  23.  *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
  24.  *  - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
  25.  * 
  26.  *  DROP Product Core implements libraries for the following:
  27.  *  - Fixed Income Analytics
  28.  *  - Loan Analytics
  29.  *  - Transaction Cost Analytics
  30.  * 
  31.  *  DROP Portfolio Core implements libraries for the following:
  32.  *  - Asset Allocation Analytics
  33.  *  - Asset Liability Management Analytics
  34.  *  - Capital Estimation Analytics
  35.  *  - Exposure Analytics
  36.  *  - Margin Analytics
  37.  *  - XVA Analytics
  38.  * 
  39.  *  DROP Computational Core implements libraries for the following:
  40.  *  - Algorithm Support
  41.  *  - Computation Support
  42.  *  - Function Analysis
  43.  *  - Model Validation
  44.  *  - Numerical Analysis
  45.  *  - Numerical Optimizer
  46.  *  - Spline Builder
  47.  *  - Statistical Learning
  48.  * 
  49.  *  Documentation for DROP is Spread Over:
  50.  * 
  51.  *  - Main                     => https://lakshmidrip.github.io/DROP/
  52.  *  - Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
  53.  *  - GitHub                   => https://github.com/lakshmiDRIP/DROP
  54.  *  - Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
  55.  *  - Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
  56.  *  - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
  57.  *  - Release Versions         => https://lakshmidrip.github.io/DROP/version.html
  58.  *  - Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
  59.  *  - Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues
  60.  *  - JUnit                    => https://lakshmidrip.github.io/DROP/junit/index.html
  61.  *  - Jacoco                   => https://lakshmidrip.github.io/DROP/jacoco/index.html
  62.  * 
  63.  *  Licensed under the Apache License, Version 2.0 (the "License");
  64.  *      you may not use this file except in compliance with the License.
  65.  *   
  66.  *  You may obtain a copy of the License at
  67.  *      http://www.apache.org/licenses/LICENSE-2.0
  68.  *  
  69.  *  Unless required by applicable law or agreed to in writing, software
  70.  *      distributed under the License is distributed on an "AS IS" BASIS,
  71.  *      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  72.  *  
  73.  *  See the License for the specific language governing permissions and
  74.  *      limitations under the License.
  75.  */

  77. /**
  78.  * <i>TTestOutcome</i> holds the Results of a Statistic Hypothesis t-Test.
  79.  *
  80.  *  <br><br>
  81.  *  <ul>
  82.  *      <li>
  83.  *          Bhattacharya, B., and D. Habtzghi (2002): Median of the p-value under the Alternate Hypothesis
  84.  *              American Statistician 56 (3) 202-206
  85.  *      </li>
  86.  *      <li>
  87.  *          Head, M. L., L. Holman, R, Lanfear, A. T. Kahn, and M. D. Jennions (2015): The Extent and
  88.  *              Consequences of p-Hacking in Science PLoS Biology 13 (3) e1002106
  89.  *      </li>
  90.  *      <li>
  91.  *          Wasserstein, R. L., and N. A. Lazar (2016): The ASA’s Statement on p-values: Context, Process,
  92.  *              and Purpose American Statistician 70 (2) 129-133
  93.  *      </li>
  94.  *      <li>
  95.  *          Wikipedia (2018): t-statistic https://en.wikipedia.org/wiki/T-statistic
  96.  *      </li>
  97.  *      <li>
  98.  *          Wikipedia (2019): p-value https://en.wikipedia.org/wiki/P-value
  99.  *      </li>
  100.  *  </ul>
  101.  *
  102.  *  <br><br>
  103.  *  <ul>
  104.  *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
  105.  *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ModelValidationAnalyticsLibrary.md">Model Validation Analytics Library</a></li>
  106.  *      <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>
  107.  *      <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/validation/hypothesis/README.md">Statistical Hypothesis Validation Test Suite</a></li>
  108.  *  </ul>
  109.  * <br><br>
  110.  *
  111.  * @author Lakshmi Krishnamurthy
  112.  */

  114. public class TTestOutcome
  115. {
  116.     private double _ensembleMean = java.lang.Double.NaN;
  117.     private double _testStatistic = java.lang.Double.NaN;
  118.     private int _sampleCount = java.lang.Integer.MIN_VALUE;
  119.     private double _ensembleVariance = java.lang.Double.NaN;
  120.     private double _ensembleTStatistics = java.lang.Double.NaN;
  121.     private double _ensembleStandardError = java.lang.Double.NaN;
  122.     private double _ensembleStandardDeviation = java.lang.Double.NaN;
  123.     private double _ensembleStandardErrorOffset = java.lang.Double.NaN;
  124.     private int _ensembleDegreesOfFreedom = java.lang.Integer.MIN_VALUE;
  125.     private double _ensemblePredictiveConfidenceInterval = java.lang.Double.NaN;

  127.     /**
  128.      * TTestOutcome Constructor
  129.      * 
  130.      * @param testStatistic Sample Test Statistic
  131.      * @param sampleCount Number of Samples in the Ensemble
  132.      * @param ensembleMean Ensemble Mean
  133.      * @param ensembleVariance Ensemble Variance
  134.      * @param ensembleStandardDeviation Ensemble Standard Deviation
  135.      * @param ensembleStandardError Ensemble Standard Error
  136.      * @param ensembleDegreesOfFreedom Ensemble Degrees of Freedom
  137.      * @param ensemblePredictiveConfidenceInterval Ensemble Predictive Confidence Interval
  138.      * @param ensembleTStatistics Ensemble t-Statistics
  139.      * @param ensembleStandardErrorOffset  Ensemble Standard Error Offset
  140.      * 
  141.      * @throws java.lang.Exception Thrown if the Inputs are Invalid
  142.      */

  144.     public TTestOutcome (
  145.         final double testStatistic,
  146.         final int sampleCount,
  147.         final double ensembleMean,
  148.         final double ensembleVariance,
  149.         final double ensembleStandardDeviation,
  150.         final double ensembleStandardError,
  151.         final int ensembleDegreesOfFreedom,
  152.         final double ensemblePredictiveConfidenceInterval,
  153.         final double ensembleTStatistics,
  154.         final double ensembleStandardErrorOffset)
  155.         throws java.lang.Exception
  156.     {
  157.         if (!org.drip.numerical.common.NumberUtil.IsValid (_testStatistic = testStatistic) ||
  158.             0 >= (_sampleCount = sampleCount) ||
  159.             !org.drip.numerical.common.NumberUtil.IsValid (_ensembleMean = ensembleMean) ||
  160.             !org.drip.numerical.common.NumberUtil.IsValid (_ensembleVariance = ensembleVariance) ||
  161.             !org.drip.numerical.common.NumberUtil.IsValid (_ensembleStandardDeviation =
  162.                 ensembleStandardDeviation) ||
  163.             !org.drip.numerical.common.NumberUtil.IsValid (_ensembleStandardError = ensembleStandardError) ||
  164.             0 > (_ensembleDegreesOfFreedom = ensembleDegreesOfFreedom) ||
  165.             !org.drip.numerical.common.NumberUtil.IsValid (_ensemblePredictiveConfidenceInterval =
  166.                 ensemblePredictiveConfidenceInterval) ||
  167.             !org.drip.numerical.common.NumberUtil.IsValid (_ensembleTStatistics = ensembleTStatistics) ||
  168.             !org.drip.numerical.common.NumberUtil.IsValid (_ensembleStandardErrorOffset =
  169.                 ensembleStandardErrorOffset))
  170.         {
  171.             throw new java.lang.Exception ("TTestOutcome Constructor => Invalid Inputs");
  172.         }
  173.     }

  175.     /**
  176.      * Retrieve the Sample Test Statistic
  177.      * 
  178.      * @return The Sample Test Statistic
  179.      */

  181.     public double testStatistic()
  182.     {
  183.         return _testStatistic;
  184.     }

  186.     /**
  187.      * Retrieve the Sample Count
  188.      * 
  189.      * @return The Sample Count
  190.      */

  192.     public int sampleCount()
  193.     {
  194.         return _sampleCount;
  195.     }

  197.     /**
  198.      * Retrieve the Ensemble Mean
  199.      * 
  200.      * @return The Ensemble Mean
  201.      */

  203.     public double ensembleMean()
  204.     {
  205.         return _ensembleMean;
  206.     }

  208.     /**
  209.      * Retrieve the Ensemble Variance
  210.      * 
  211.      * @return The Ensemble Variance
  212.      */

  214.     public double ensembleVariance()
  215.     {
  216.         return _ensembleVariance;
  217.     }

  219.     /**
  220.      * Retrieve the Ensemble Standard Deviation
  221.      * 
  222.      * @return The Ensemble Standard Deviation
  223.      */

  225.     public double ensembleStandardDeviation()
  226.     {
  227.         return _ensembleStandardDeviation;
  228.     }

  230.     /**
  231.      * Retrieve the Ensemble Standard Error
  232.      * 
  233.      * @return The Ensemble Standard Error
  234.      */

  236.     public double ensembleStandardError()
  237.     {
  238.         return _ensembleStandardError;
  239.     }

  241.     /**
  242.      * Retrieve the Ensemble Degrees of Freedom
  243.      * 
  244.      * @return The Ensemble Degrees of Freedom
  245.      */

  247.     public int ensembleDegreesOfFreedom()
  248.     {
  249.         return _ensembleDegreesOfFreedom;
  250.     }

  252.     /**
  253.      * Retrieve the Ensemble Predictive Confidence Interval
  254.      * 
  255.      * @return The Ensemble Predictive Confidence Interval
  256.      */

  258.     public double ensemblePredictiveConfidenceInterval()
  259.     {
  260.         return _ensemblePredictiveConfidenceInterval;
  261.     }

  263.     /**
  264.      * Retrieve the Ensemble t-Statistics
  265.      * 
  266.      * @return The Ensemble t-Statistics
  267.      */

  269.     public double ensembleTStatistics()
  270.     {
  271.         return _ensembleTStatistics;
  272.     }

  274.     /**
  275.      * Retrieve the Ensemble Standard Error Offset
  276.      * 
  277.      * @return The Ensemble Standard Error Offset
  278.      */

  280.     public double ensembleStandardErrorOffset()
  281.     {
  282.         return _ensembleStandardErrorOffset;
  283.     }
  284. }
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