NAG FL Interface
g08cbf (test_​ks_​1sample)

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1 Purpose

g08cbf performs the one sample Kolmogorov–Smirnov test, using one of the distributions provided.

2 Specification

Fortran Interface
Subroutine g08cbf ( n, x, dist, par, estima, ntype, d, z, p, sx, ifail)
Integer, Intent (In) :: n, ntype
Integer, Intent (Inout) :: ifail
Real (Kind=nag_wp), Intent (In) :: x(n)
Real (Kind=nag_wp), Intent (Inout) :: par(2)
Real (Kind=nag_wp), Intent (Out) :: d, z, p, sx(n)
Character (*), Intent (In) :: dist
Character (1), Intent (In) :: estima
C Header Interface
#include <nag.h>
void  g08cbf_ (const Integer *n, const double x[], const char *dist, double par[], const char *estima, const Integer *ntype, double *d, double *z, double *p, double sx[], Integer *ifail, const Charlen length_dist, const Charlen length_estima)
The routine may be called by the names g08cbf or nagf_nonpar_test_ks_1sample.

3 Description

The data consist of a single sample of n observations denoted by x 1 , x 2 , , x n . Let S n ( x (i) ) and F 0 ( x (i) ) represent the sample cumulative distribution function and the theoretical (null) cumulative distribution function respectively at the point x (i) where x (i) is the i th smallest sample observation.
The Kolmogorov–Smirnov test provides a test of the null hypothesis H 0 : the data are a random sample of observations from a theoretical distribution specified by you against one of the following alternative hypotheses:
  1. (i) H 1 : the data cannot be considered to be a random sample from the specified null distribution.
  2. (ii) H 2 : the data arise from a distribution which dominates the specified null distribution. In practical terms, this would be demonstrated if the values of the sample cumulative distribution function S n (x) tended to exceed the corresponding values of the theoretical cumulative distribution function F 0 (x) .
  3. (iii) H 3 : the data arise from a distribution which is dominated by the specified null distribution. In practical terms, this would be demonstrated if the values of the theoretical cumulative distribution function F 0 (x) tended to exceed the corresponding values of the sample cumulative distribution function S n (x) .
One of the following test statistics is computed depending on the particular alternative null hypothesis specified (see the description of the argument ntype in Section 5).
For the alternative hypothesis H 1 .
For the alternative hypothesis H 2 .
For the alternative hypothesis H 3 .
The standardized statistic Z = D × n is also computed where D may be D n , D n + or D n - depending on the choice of the alternative hypothesis. This is the standardized value of D with no correction for continuity applied and the distribution of Z converges asymptotically to a limiting distribution, first derived by Kolmogorov (1933), and then tabulated by Smirnov (1948). The asymptotic distributions for the one-sided statistics were obtained by Smirnov (1933).
The probability, under the null hypothesis, of obtaining a value of the test statistic as extreme as that observed, is computed. If n100 an exact method given by Conover (1980), is used. Note that the method used is only exact for continuous theoretical distributions and does not include Conover's modification for discrete distributions. This method computes the one-sided probabilities. The two-sided probabilities are estimated by doubling the one-sided probability. This is a good estimate for small p , that is p0.10 , but it becomes very poor for larger p . If n>100 then p is computed using the Kolmogorov–Smirnov limiting distributions, see Feller (1948), Kendall and Stuart (1973), Kolmogorov (1933), Smirnov (1933) and Smirnov (1948).

4 References

Conover W J (1980) Practical Nonparametric Statistics Wiley
Feller W (1948) On the Kolmogorov–Smirnov limit theorems for empirical distributions Ann. Math. Statist. 19 179–181
Kendall M G and Stuart A (1973) The Advanced Theory of Statistics (Volume 2) (3rd Edition) Griffin
Kolmogorov A N (1933) Sulla determinazione empirica di una legge di distribuzione Giornale dell' Istituto Italiano degli Attuari 4 83–91
Siegel S (1956) Non-parametric Statistics for the Behavioral Sciences McGraw–Hill
Smirnov N (1933) Estimate of deviation between empirical distribution functions in two independent samples Bull. Moscow Univ. 2(2) 3–16
Smirnov N (1948) Table for estimating the goodness of fit of empirical distributions Ann. Math. Statist. 19 279–281

5 Arguments

1: n Integer Input
On entry: n, the number of observations in the sample.
Constraint: n3 .
2: x(n) Real (Kind=nag_wp) array Input
On entry: the sample observations x 1 , x 2 , , x n .
Constraint: the sample observations supplied must be consistent, in the usual manner, with the null distribution chosen, as specified by the arguments dist and par. For further details see Section 9.
3: dist Character(*) Input
On entry: the theoretical (null) distribution from which it is suspected the data may arise.
dist='U'
The uniform distribution over (a,b).
dist='N'
The Normal distribution with mean μ and variance σ2.
dist='G'
The gamma distribution with shape parameterα and scale parameter β, where the mean =αβ.
dist='BE'
The beta distribution with shape parameters α and β, where the mean =α/(α+β).
dist='BI'
The binomial distribution with the number of trials, m, and the probability of a success, p.
dist='E'
The exponential distribution with parameter λ, where the mean =1/λ.
dist='P'
The Poisson distribution with parameter μ, where the mean =μ.
dist='NB'
The negative binomial distribution with the number of trials, m, and the probability of success, p.
dist='GP'
The generalized Pareto distribution with shape parameter ξ and scale β.
Any number of characters may be supplied as the actual parameter, however only the characters, maximum 2, required to uniquely identify the distribution are referenced.
Constraint: dist='U', 'N', 'G', 'BE', 'BI', 'E', 'P', 'NB' or 'GP'.
4: par(2) Real (Kind=nag_wp) array Input/Output
On entry: if estima='S', par must contain the known values of the parameter(s) of the null distribution as follows.
If a uniform distribution is used, par(1) and par(2) must contain the boundaries a and b respectively.
If a Normal distribution is used, par(1) and par(2) must contain the mean, μ, and the variance, σ2, respectively.
If a gamma distribution is used, par(1) and par(2) must contain the parameters α and β respectively.
If a beta distribution is used, par(1) and par(2) must contain the parameters α and β respectively.
If a binomial distribution is used, par(1) and par(2) must contain the parameters m and p respectively.
If an exponential distribution is used, par(1) must contain the parameter λ.
If a Poisson distribution is used, par(1) must contain the parameter μ.
If a negative binomial distribution is used, par(1) and par(2) must contain the parameters m and p respectively.
If a generalized Pareto distribution is used, par(1) and par(2) must contain the parameters ξ and β respectively.
If estima='E', par need not be set except when the null distribution requested is either the binomial or the negative binomial distribution in which case par(1) must contain the parameter m.
On exit: if estima='S', par is unchanged; if estima='E', and dist='BI' or dist='NB' then par(2) is estimated from the data; otherwise par(1) and par(2) are estimated from the data.
Constraints:
  • if dist='U', par(1)<par(2);
  • if dist='N', par(2)>0.0;
  • if dist='G', par(1)>0.0 and par(2)>0.0;
  • if dist='BE', par(1)>0.0 and par(2)>0.0 and par(1)106 and par(2)106;
  • if dist='BI', par(1)1.0 and 0.0<par(2)<1.0 and par(1)×par(2)×(1.0-par(2))106 and par(1)<1/eps, where eps=machine precision, see x02ajf;
  • if dist='E', par(1)>0.0;
  • if dist='P', par(1)>0.0 and par(1)106;
  • if dist='NB', par(1)1.0 and 0.0<par(2)<1.0 and par(1)× (1.0-par(2))/ (par(2)×par(2)) 106 and par(1)<1/eps, where eps=machine precision, see x02ajf;
  • if dist='GP', par(2)>0.
5: estima Character(1) Input
On entry: estima must specify whether values of the parameters of the null distribution are known or are to be estimated from the data.
estima='S'
Values of the parameters will be supplied in the array par described above.
estima='E'
Parameters are to be estimated from the data except when the null distribution requested is the binomial distribution or the negative binomial distribution in which case the first parameter, m, must be supplied in par(1) and only the second parameter, p, is estimated from the data.
Constraint: estima='S' or 'E'.
6: ntype Integer Input
On entry: the test statistic to be calculated, i.e., the choice of alternative hypothesis.
ntype=1
Computes D n , to test H 0 against H 1 ,
ntype=2
Computes D n + , to test H 0 against H 2 ,
ntype=3
Computes D n - , to test H 0 against H 3 .
Constraint: ntype=1, 2 or 3.
7: d Real (Kind=nag_wp) Output
On exit: the Kolmogorov–Smirnov test statistic ( D n , D n + or D n - according to the value of ntype).
8: z Real (Kind=nag_wp) Output
On exit: a standardized value, Z , of the test statistic, D , without any correction for continuity.
9: p Real (Kind=nag_wp) Output
On exit: the probability, p , associated with the observed value of D where D may be D n , D n + or D n - depending on the value of ntype (see Section 3).
10: sx(n) Real (Kind=nag_wp) array Output
On exit: the sample observations, x1,x2,,xn, sorted in ascending order.
11: ifail Integer Input/Output
On entry: ifail must be set to 0, −1 or 1 to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of 0 causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of −1 means that an error message is printed while a value of 1 means that it is not.
If halting is not appropriate, the value −1 or 1 is recommended. If message printing is undesirable, then the value 1 is recommended. Otherwise, the value 0 is recommended. When the value -1 or 1 is used it is essential to test the value of ifail on exit.
On exit: ifail=0 unless the routine detects an error or a warning has been flagged (see Section 6).

6 Error Indicators and Warnings

If on entry ifail=0 or −1, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
ifail=1
On entry, n=value.
Constraint: n3.
ifail=2
On entry, dist=value was an illegal value.
ifail=3
On entry, ntype=value.
Constraint: ntype=1, 2 or 3.
ifail=4
On entry, estima=value was an illegal value.
ifail=5
On entry, dist='BI' and m=par(1)=value.
Note that m must always be supplied.
Constraint: for the binomial distribution, 1par(1)<1/eps, where eps=machine precision, see x02ajf.
On entry, dist='NB' and m=par(1)=value.
Note that m must always be supplied.
Constraint: for the negative binomial distribution, 1par(1)<1/eps, where eps=machine precision, see x02ajf.
On entry, estima='S' and par(1)=value; par(2)=value.
Constraint: for the beta distribution, 0<par(1) and par(2)1000000.
On entry, estima='S' and par(1)=value; par(2)=value.
Constraint: for the gamma distribution, par(1) and par(2)>0.
On entry, estima='S' and par(1)=value; par(2)=value.
Constraint: for the generalized Pareto distribution with par(1)<0, 0x(i)-par(2)/par(1), for i=1,2,,n.
On entry, estima='S' and par(1)=value; par(2)=value.
Constraint: for the uniform distribution, par(1)<par(2).
On entry, estima='S' and par(1)=value.
Constraint: for the exponential distribution, par(1)>0.
On entry, estima='S' and par(1)=value.
Constraint: for the Poisson distribution, 0<par(1)<1000000.
On entry, estima='S' and par(2)=value.
Constraint: for the binomial distribution, 0<par(2)<1.
On entry, estima='S' and par(2)=value.
Constraint: for the generalized Pareto distribution, par(2)>0.
On entry, estima='S' and par(2)=value.
Constraint: for the negative binomial distribution, 0<par(2)<1.
On entry, estima='S' and par(2)=value.
Constraint: for the Normal distribution, par(2)>0.
ifail=6
On entry, dist='U' and at least one observation is illegal.
Constraint: par(1)x(i)par(2), for i=1,2,,n.
On entry, dist='G', 'E', 'P', 'NB' or 'GP' and at least one observation is negative.
Constraint: x(i)0, for i=1,2,,n.
On entry, dist='BE' and at least one observation is illegal.
Constraint: 0x(i)1, for i=1,2,,n.
On entry, dist='BI' and all observations are zero or m.
Constraint: at least one 0.0<x(i)<par(1), for i=1,2,,n.
On entry, dist='BI' and at least one observation is illegal.
Constraint: 0x(i)par(1), for i=1,2,,n.
On entry, dist='E' or 'P' and all observations are zero.
Constraint: at least one x(i)>0, for i=1,2,,n.
On entry, dist='GP' and estima='E'.
The parameter estimates are invalid; the data may not be from the generalized Pareto distribution.
ifail=7
On entry, dist='U', 'N', 'G', 'BE' or 'GP', estima='E' and the whole sample is constant. Thus the variance is zero.
ifail=8
On entry, dist='BI', par(1)=value, par(2)=value.
The variance par(1)×par(2)×(1-par(2)) exceeds 1000000.
On entry, dist='NB', par(1)=value, par(2)=value.
The variance par(1)×(1-par(2))/(par(2)×par(2)) exceeds 1000000.
ifail=9
On entry, dist='G' and in the computation of the incomplete gamma function by s14baf the convergence of the Taylor series or Legendre continued fraction fails within 600 iterations.
ifail=-99
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.
ifail=-399
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.
ifail=-999
Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

The approximation for p , given when n>100 , has a relative error of at most 2.5% for most cases. The two-sided probability is approximated by doubling the one-sided probability. This is only good for small p , i.e., p<0.10 but very poor for large p . The error is always on the conservative side, that is the tail probability, p , is over estimated.

8 Parallelism and Performance

g08cbf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

The time taken by g08cbf increases with n until n>100 at which point it drops and then increases slowly with n . The time may also depend on the choice of null distribution and on whether or not the parameters are to be estimated.
The data supplied in the argument x must be consistent with the chosen null distribution as follows:

10 Example

The following example program reads in a set of data consisting of 30 observations. The Kolmogorov–Smirnov test is then applied twice, firstly to test whether the sample is taken from a uniform distribution, U(0,2), and secondly to test whether the sample is taken from a Normal distribution where the mean and variance are estimated from the data. In both cases we are testing against H1; that is, we are doing a two tailed test. The values of d, z and p are printed for each case.

10.1 Program Text

Program Text (g08cbfe.f90)

10.2 Program Data

Program Data (g08cbfe.d)

10.3 Program Results

Program Results (g08cbfe.r)