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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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