nag_outlier_peirce (g07gac) (PDF version)
g07 Chapter Contents
g07 Chapter Introduction
NAG Library Manual

NAG Library Function Document

nag_outlier_peirce (g07gac)


    1  Purpose
    7  Accuracy

1  Purpose

nag_outlier_peirce (g07gac) identifies outlying values using Peirce's criterion.

2  Specification

#include <nag.h>
#include <nagg07.h>
void  nag_outlier_peirce (Integer n, Integer p, const double y[], double mean, double var, Integer iout[], Integer *niout, Integer ldiff, double diff[], double llamb[], NagError *fail)

3  Description

nag_outlier_peirce (g07gac) flags outlying values in data using Peirce's criterion. Let
Peirce's method flags yi as a potential outlier if yi-μx, where x=σ2z and z is obtained from the solution of
Rm = λ m-n mm n-m n-m nn (1)
R = 2 exp z2 - 1 2 1- Φz (2)
and Φ is the cumulative distribution function for the standard Normal distribution.
As σ~2 is unknown an assumption is made that the relationship between σ~2 and σ2, hence λ, depends only on the sum of squares of the rejected observations and the ratio estimated as
λ2 = n-p-m z2 n-p-m  
which gives
z2 = 1+ n-p-m m 1-λ2 (3)
A value for the cutoff x is calculated iteratively. An initial value of R=0.2 is used and a value of λ is estimated using equation (1). Equation (3) is then used to obtain an estimate of z and then equation (2) is used to get a new estimate for R. This process is then repeated until the relative change in z between consecutive iterations is ε, where ε is machine precision.
By construction, the cutoff for testing for m+1 potential outliers is less than the cutoff for testing for m potential outliers. Therefore Peirce's criterion is used in sequence with the existence of a single potential outlier being investigated first. If one is found, the existence of two potential outliers is investigated etc.
If one of a duplicate series of observations is flagged as an outlier, then all of them are flagged as outliers.

4  References

Gould B A (1855) On Peirce's criterion for the rejection of doubtful observations, with tables for facilitating its application The Astronomical Journal 45
Peirce B (1852) Criterion for the rejection of doubtful observations The Astronomical Journal 45

5  Arguments

1:     n IntegerInput
On entry: n, the number of observations.
Constraint: n3.
2:     p IntegerInput
On entry: p, the number of parameters in the model used in obtaining the y. If y is an observed set of values, as opposed to the residuals from fitting a model with p parameters, then p should be set to 1, i.e., as if a model just containing the mean had been used.
Constraint: 1pn-2.
3:     y[n] const doubleInput
On entry: y, the data being tested.
4:     mean doubleInput
On entry: if var>0.0, mean must contain μ, the mean of y, otherwise mean is not referenced and the mean is calculated from the data supplied in y.
5:     var doubleInput
On entry: if var>0.0, var must contain σ2, the variance of y, otherwise the variance is calculated from the data supplied in y.
6:     iout[n] IntegerOutput
On exit: the indices of the values in y sorted in descending order of the absolute difference from the mean, therefore y[iout[i-2]-1] - μ y[iout[i-1]-1] - μ , for i=2,3,,n.
7:     niout Integer *Output
On exit: the number of potential outliers. The indices for these potential outliers are held in the first niout elements of iout. By construction there can be at most n-p-1 values flagged as outliers.
8:     ldiff IntegerInput
On entry: the maximum number of values to be returned in arrays diff and llamb.
If ldiff0, arrays diff and llamb are not referenced and both diff and llamb may be NULL.
9:     diff[ldiff] doubleOutput
On exit: if diff is not NULL then diff[i-1] holds y-μ-σ2z for observation y[iout[i-1]-1], for i=1,2,,minldiff,niout+1,n-p-1.
10:   llamb[ldiff] doubleOutput
On exit: if llamb is not NULL then llamb[i-1] holds logλ2 for observation y[iout[i-1]-1], for i=1,2,,minldiff,niout+1,n-p-1.
11:   fail NagError *Input/Output
The NAG error argument (see Section 2.7 in How to Use the NAG Library and its Documentation).

6  Error Indicators and Warnings

Dynamic memory allocation failed.
See Section in How to Use the NAG Library and its Documentation for further information.
On entry, argument value had an illegal value.
On entry, n=value.
Constraint: n3.
On entry, p=value and n=value.
Constraint: 1pn-2.
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.
An unexpected error has been triggered by this function. Please contact NAG.
See Section 2.7.6 in How to Use the NAG Library and its Documentation for further information.
Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.

7  Accuracy

Not applicable.

8  Parallelism and Performance

nag_outlier_peirce (g07gac) is not threaded in any implementation.

9  Further Comments

One problem with Peirce's algorithm as implemented in nag_outlier_peirce (g07gac) is the assumed relationship between σ2, the variance using the full dataset, and σ~2, the variance with the potential outliers removed. In some cases, for example if the data y were the residuals from a linear regression, this assumption may not hold as the regression line may change significantly when outlying values have been dropped resulting in a radically different set of residuals. In such cases nag_outlier_peirce_two_var (g07gbc) should be used instead.

10  Example

This example reads in a series of data and flags any potential outliers.
The dataset used is from Peirce's original paper and consists of fifteen observations on the vertical semidiameter of Venus.

10.1  Program Text

Program Text (g07gace.c)

10.2  Program Data

Program Data (g07gace.d)

10.3  Program Results

Program Results (g07gace.r)

nag_outlier_peirce (g07gac) (PDF version)
g07 Chapter Contents
g07 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2016