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java.lang.Objectumontreal.iro.lecuyer.gof.FBar
public class FBar
This class is similar to FDist, except that it provides static methods
to compute or approximate the complementary distribution function of X,
which we define as
bar(F)(x) = P[X >= x], instead of
F(x) = P[X <= x].
Note that with our definition of bar(F), one has
bar(F)(x) = 1 - F(x) for continuous distributions and
bar(F)(x) = 1 - F(x - 1) for discrete distributions over the integers.
This is non-standard but we find it convenient.
For more details about the specific distributions,
see the class FDist.
When F(x) is very close to 1, these methods generally provide much
more precise values of
bar(F)(x) than using 1 - F(x) where F(x) is
computed by a method from FDist.
| Method Summary | |
|---|---|
static double |
andersonDarling(int n,
double x)
Returns 1.0 - FDist.andersonDarling (n, x). |
static double |
cramerVonMises(int n,
double x)
Returns 1.0 - FDist.cramerVonMises (n, x). |
static double |
kolmogorovSmirnov(int n,
double x)
Returns 1.0 - FDist.kolmogorovSmirnov (n, x). |
static double |
kolmogorovSmirnovPlus(int n,
double x)
Returns 1.0 - FDist.kolmogorovSmirnovPlus (n, x). |
static double |
scan(int n,
double d,
int m)
Return P[SN(d ) >= m], where SN(d ) is the scan statistic. |
static double |
watsonG(int n,
double x)
Returns 1.0 - FDist.watsonG (n, x). |
static double |
watsonU(int n,
double x)
Returns 1.0 - FDist.watsonU (n, x). |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Method Detail |
|---|
public static double kolmogorovSmirnov(int n,
double x)
FDist.kolmogorovSmirnov (n, x).
n - sample sizex - Kolmogorov-Smirnov statistic
public static double kolmogorovSmirnovPlus(int n,
double x)
FDist.kolmogorovSmirnovPlus (n, x).
n - sample sizex - Kolmogorov-Smirnov statistic
public static double cramerVonMises(int n,
double x)
FDist.cramerVonMises (n, x).
n - sample sizex - Cramér-von Mises statistic
public static double watsonU(int n,
double x)
FDist.watsonU (n, x).
n - sample sizex - Watson statistic
public static double watsonG(int n,
double x)
FDist.watsonG (n, x).
n - sample sizex - Watson statistic
public static double andersonDarling(int n,
double x)
FDist.andersonDarling (n, x).
n - sample sizex - Anderson-Darling statistic
public static double scan(int n,
double d,
int m)
n - sample size ( >= 2)d - length of the test interval (∈(0, 1))m - scan statistic
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