forked from kscience/kmath
- Zelen-Severo CDF aproximation
- p-value for varianceRatioTest
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@ -9,10 +9,11 @@ import space.kscience.kmath.operations.DoubleField.pow
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import space.kscience.kmath.operations.algebra
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import space.kscience.kmath.operations.bufferAlgebra
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import space.kscience.kmath.operations.fold
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import kotlin.math.absoluteValue
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// TODO: add p-value with formula: 2*(1 - cdf(|zScore|))
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public data class VarianceRatioTestResult(val varianceRatio: Double=1.0, val zScore: Double=0.0)
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public data class VarianceRatioTestResult(val varianceRatio: Double=1.0, val zScore: Double=0.0, val pValue: Double=0.5)
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/**
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* Container class for Variance Ratio Test result:
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* ratio itself, corresponding Z-score, also it's p-value
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@ -23,6 +24,7 @@ public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic:
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/**
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* Calculates the Z-statistic and the p-value for the Lo and MacKinlay's Variance Ratio test (1987)
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* under Homoscedastic or Heteroscedstic assumptions
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* with two-sided p-value test
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* https://ssrn.com/abstract=346975
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* **/
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@ -63,6 +65,11 @@ public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic:
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}
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val zScore = (varianceRatio - 1) / phi.pow(0.5)
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return VarianceRatioTestResult(varianceRatio, zScore)
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val pValue = 2*(1 - zSNormalCDF(zScore.absoluteValue))
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return VarianceRatioTestResult(varianceRatio, zScore, pValue)
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}
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}
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@ -0,0 +1,24 @@
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/*
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* Copyright 2018-2023 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.series
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import space.kscience.kmath.operations.DoubleField.pow
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import kotlin.math.PI
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import kotlin.math.absoluteValue
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import kotlin.math.exp
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public fun zSNormalCDF(x: Double): Double {
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/**
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* Zelen & Severo approximation for the standard normal CDF.
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* The error is bounded by 7.5 * 10e-8.
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* */
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val t = 1 / (1 + 0.2316419 * x.absoluteValue)
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val summ = 0.319381530*t - 0.356563782*t.pow(2) + 1.781477937*t.pow(3) - 1.821255978*t.pow(4) + 1.330274429*t.pow(5)
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val temp = summ * exp(-x.absoluteValue.pow(2) / 2) / (2 * PI).pow(0.5)
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return if (x >= 0) 1 - temp else temp
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}
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@ -21,9 +21,11 @@ class TestVarianceRatioTest {
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assertEquals(1.818181, resultHomo.varianceRatio, 1e-6)
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// homoscedastic zScore
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assertEquals(2.587318, resultHomo.zScore, 1e-6)
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assertEquals(.0096, resultHomo.pValue, 1e-4)
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val resultHetero = varianceRatioTest(monotonicData, 2, homoscedastic = false)
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// heteroscedastic zScore
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assertEquals(0.819424, resultHetero.zScore, 1e-6)
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assertEquals(.4125, resultHetero.pValue, 1e-4)
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}
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}
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@ -35,9 +37,11 @@ class TestVarianceRatioTest {
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assertEquals(0.0, resultHomo.varianceRatio, 1e-6)
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// homoscedastic zScore
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assertEquals(-3.162277, resultHomo.zScore, 1e-6)
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assertEquals(.0015, resultHomo.pValue, 1e-4)
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val resultHetero = varianceRatioTest(volatileData, 2, homoscedastic = false)
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// heteroscedastic zScore
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assertEquals(-1.0540925, resultHetero.zScore, 1e-6)
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assertEquals(.2918, resultHetero.pValue, 1e-4)
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}
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}
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@ -62,6 +66,7 @@ class TestVarianceRatioTest {
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val result = varianceRatioTest(zeroVolData, 4)
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assertEquals(1.0, result.varianceRatio, 1e-6)
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assertEquals(0.0, result.zScore, 1e-6)
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assertEquals(0.5, result.pValue, 1e-4)
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}
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}
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}
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