Package org.drip.measure.statistics

Class UnivariateMoments

java.lang.Object
org.drip.measure.statistics.UnivariateMoments
public class UnivariateMoments
extends java.lang.Object
UnivariateMoments generates and holds the Specified Univariate Series Mean, Variance, and a few selected Moments. It provides the following Functionality:
  • Construct a UnivariateMoments Instance for the specified Series #1
  • Construct a UnivariateMoments Instance for the specified Series #2
  • Retrieve the Series Name
  • Retrieve the Number of Samples
  • Retrieve the Series Mean
  • Retrieve the Series Variance
  • Retrieve the Series Standard Deviation
  • Retrieve the Series Standard Error
  • Retrieve the Moments Map
  • Compute the Series t-Statistic around the Series Hypothesis Pivot
  • Compute the Series t-Statistic for Hypothesis Pivot = 0 (e.g., the False Positive NULL Hypothesis for for Homoscedastic Univariate Linear Regression)
  • Estimate the Offset in Terms of the Number of Standard Errors
  • Retrieve the Degrees of Freedom
  • Compute the Predictive Confidence Level

Module Computational Core Module
Library Numerical Analysis Library
Project Rd Continuous/Discrete Probability Measures
Package R1 Rd Thin Thick Moments

Author:
Lakshmi Krishnamurthy

  • Method Summary

    Modifier and Type Method Description
    int degreesOfFreedom()
    Retrieve the Degrees of Freedom
    double mean()
    Retrieve the Series Mean
    java.util.Map<java.lang.Integer,​java.lang.Double> momentMap()
    Retrieve the Moments Map
    java.lang.String name()
    Retrieve the Series Name
    int numSample()
    Retrieve the Number of Samples
    double predictiveConfidenceLevel()
    Compute the Predictive Confidence Level
    static UnivariateMoments Standard​(java.lang.String name, double[] entryArray)
    Construct a UnivariateMoments Instance for the specified Series #2
    static UnivariateMoments Standard​(java.lang.String name, double[] entryArray, int[] orderStatisticArray)
    Construct a UnivariateMoments Instance for the specified Series #1
    double standardErrorOffset​(double x)
    Estimate the Offset in Terms of the NUmber of Standard Errors
    double stdDev()
    Retrieve the Series Standard Deviation
    double stdError()
    Retrieve the Series Standard Error
    double tStatistic()
    Compute the Series t-Statistic for Hypothesis Pivot = 0 (e.g., the False Positive NULL Hypothesis for for Homoscedastic Univariate Linear Regression)
    double tStatistic​(double hypothesisPivot)
    Compute the Series t-Statistic around the Series Hypothesis Pivot
    double variance()
    Retrieve the Series Variance

    Methods inherited from class java.lang.Object

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

  • Method Details

    • Standard

      public static final UnivariateMoments Standard​(java.lang.String name, double[] entryArray, int[] orderStatisticArray)
      Construct a UnivariateMoments Instance for the specified Series #1
      Parameters:
      name - Series Name
      entryArray - Series Entry
      orderStatisticArray - Array of Moment Order Statistic to be Calculated
      Returns:
      The UnivariateMoments Instance

    • Standard

      public static final UnivariateMoments Standard​(java.lang.String name, double[] entryArray)
      Construct a UnivariateMoments Instance for the specified Series #2
      Parameters:
      name - Series Name
      entryArray - Series Entry
      Returns:
      The UnivariateMoments Instance

    • name

      public java.lang.String name()
      Retrieve the Series Name
      Returns:
      The Series Name

    • numSample

      public int numSample()
      Retrieve the Number of Samples
      Returns:
      The Number of Samples

    • mean

      public double mean()
      Retrieve the Series Mean
      Returns:
      The Series Mean

    • variance

      public double variance()
      Retrieve the Series Variance
      Returns:
      The Series Variance

    • stdDev

      public double stdDev()
      Retrieve the Series Standard Deviation
      Returns:
      The Series Standard Deviation

    • stdError

      public double stdError()
      Retrieve the Series Standard Error
      Returns:
      The Series Standard Error

    • momentMap

      public java.util.Map<java.lang.Integer,​java.lang.Double> momentMap()
      Retrieve the Moments Map
      Returns:
      The Map of Moments

    • tStatistic

      public double tStatistic​(double hypothesisPivot) throws java.lang.Exception
      Compute the Series t-Statistic around the Series Hypothesis Pivot
      Parameters:
      hypothesisPivot - The Series Hypothesis Pivot
      Returns:
      The Series t-Statistic around the Series Hypothesis Pivot
      Throws:
      java.lang.Exception - Thrown if the Inputs are Invalid

    • tStatistic

      public double tStatistic() throws java.lang.Exception
      Compute the Series t-Statistic for Hypothesis Pivot = 0 (e.g., the False Positive NULL Hypothesis for for Homoscedastic Univariate Linear Regression)
      Returns:
      The Series t-Statistic
      Throws:
      java.lang.Exception - Thrown if the Inputs are Invalid

    • standardErrorOffset

      public double standardErrorOffset​(double x) throws java.lang.Exception
      Estimate the Offset in Terms of the NUmber of Standard Errors
      Parameters:
      x - The Observation Point
      Returns:
      The Offset in Terms of the NUmber of Standard Errors
      Throws:
      java.lang.Exception - Thrown if the Inputs are Invalid

    • degreesOfFreedom

      public int degreesOfFreedom()
      Retrieve the Degrees of Freedom
      Returns:
      The Degrees of Freedom

    • predictiveConfidenceLevel

      public double predictiveConfidenceLevel()
      Compute the Predictive Confidence Level
      Returns:
      The Predictive Confidence Level