Documentation of 'umontreal.iro.lecuyer.gof.FDist' Java class
FDist
umontreal.iro.lecuyer.gof

Class FDist



  • public class FDist
    extends java.lang.Object
    This class provides methods to compute (or approximate) the distribution functions of special types of goodness-of-fit test statistics.
    • Method Summary

      All Methods Static Methods Concrete Methods 
      Modifier and Type Method and Description
      static double kolmogorovSmirnovPlusJumpOne(int N, double a, double x)
      Similar to KolmogorovSmirnovPlusDist but for the case where the distribution function F has a jump of size a at a given point x0, is zero at the left of x0, and is continuous at the right of x0.
      static double scan(int N, double d, int m)
      Returns F(m), the distribution function of the scan statistic with parameters N and d, evaluated at m.
      • Methods inherited from class java.lang.Object

        equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    • Method Detail

      • kolmogorovSmirnovPlusJumpOne

        public static double kolmogorovSmirnovPlusJumpOne(int N,
                                                          double a,
                                                          double x)
        Similar to KolmogorovSmirnovPlusDist but for the case where the distribution function F has a jump of size a at a given point x0, is zero at the left of x0, and is continuous at the right of x0. Restriction: 0 < a < 1.
        Parameters:
        N - sample size
        a - size of the jump
        x - positive or negative Kolmogorov-Smirnov statistic
        Returns:
        the distribution function of the statistic evaluated at x
      • scan

        public static double scan(int N,
                                  double d,
                                  int m)
        Returns F(m), the distribution function of the scan statistic with parameters N and d, evaluated at m. For a description of this statistic and its distribution, see scan, which computes its complementary distribution bar(F)(m) = 1 - F(m - 1).
        Parameters:
        N - sample size ( >= 2)
        d - length of the test interval (∈(0, 1))
        m - scan statistic
        Returns:
        the distribution function of the statistic evaluated at m

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