NAG Library Manual, Mark 27
Interfaces:  FL   CL   CPP   AD 
NAG CL Interface Introduction
G08 (Nonpar) Chapter Contents
G08 (Nonpar) Chapter Introduction
g08ch:  FL   CL   CPP   AD 

NAG CL Interface
g08chc (gofstat_​anddar)

NAG Library Manual, Mark 27
Interfaces:  FL   CL   CPP   AD 
NAG CL Interface Introduction
G08 (Nonpar) Chapter Contents
G08 (Nonpar) Chapter Introduction
g08ch:  FL   CL   CPP   AD 

▸▿ Contents

© The Numerical Algorithms Group Ltd. 2019

1 Purpose

g08chc calculates the Anderson–Darling goodness-of-fit test statistic.

2 Specification

#include <nag.h>
double  g08chc (Integer n, Nag_Boolean issort, double y[], NagError *fail)
The function may be called by the names: g08chc, nag_nonpar_gofstat_anddar or nag_anderson_darling_stat.

3 Description

Denote by A2 the Anderson–Darling test statistic for n observations y1,y2,…,yn of a variable Y assumed to be standard uniform and sorted in ascending order, then:
A2 = -n-S ;  
where:
S = ∑ i=1 n 2i-1 n ln⁡yi + ln 1- y n-i+1 .  
When observations of a random variable X are non-uniformly distributed, the probability integral transformation (PIT):
Y=FX ,  
where F is the cumulative distribution function of the distribution of interest, yields a uniformly distributed random variable Y. The PIT is true only if all parameters of a distribution are known as opposed to estimated; otherwise it is an approximation.

4 References

Anderson T W and Darling D A (1952) Asymptotic theory of certain ‘goodness-of-fit’ criteria based on stochastic processes Annals of Mathematical Statistics 23 193–212

5 Arguments

1: n – Integer Input
On entry: n, the number of observations.
Constraint: n>1.
2: issort – Nag_Boolean Input
On entry: set issort=Nag_TRUE if the observations are sorted in ascending order; otherwise the function will sort the observations.
3: y[n] – double Input/Output
On entry: yi, for i=1,2,…,n, the n observations.
On exit: if issort=Nag_FALSE, the data sorted in ascending order; otherwise the array is unchanged.
Constraint: if issort=Nag_TRUE, the values must be sorted in ascending order. Each yi must lie in the interval 0,1.
4: fail – NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_BOUND
The data in y must lie in the interval 0,1.
NE_INT
On entry, n=value.
Constraint: n>1.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_NOT_INCREASING
issort=Nag_TRUE and the data in y is not sorted in ascending order.

7 Accuracy

Not applicable.

8 Parallelism and Performance

g08chc 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 function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

None.

10 Example

This example calculates the A2 statistic for data assumed to arise from an exponential distribution with a sample parameter estimate and simulates its p-value using the NAG basic random number generator.

10.1 Program Text

Program Text (g08chce.c)

10.2 Program Data

Program Data (g08chce.d)

10.3 Program Results

Program Results (g08chce.r)
NAG Library Manual, Mark 27
Interfaces:  FL   CL   CPP   AD 
NAG CL Interface Introduction
G08 (Nonpar) Chapter Contents
G08 (Nonpar) Chapter Introduction
g08ch:  FL   CL   CPP   AD 
© The Numerical Algorithms Group Ltd. 2019