NAG Library Function Document

nag_outlier_peirce (g07gac)

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

$y$ denote a vector of $n$ observations (for example the residuals) obtained from a model with $p$ parameters,
$m$ denote the number of potential outlying values,
$μ$ and $σ^{2}$ denote the mean and variance of $y$ respectively,
$\tilde{y}$ denote a vector of length $n - m$ constructed by dropping the $m$ values from $y$ with the largest value of $|y_{i} - μ|$ ,
${\tilde{σ}}^{2}$ denote the (unknown) variance of $\tilde{y}$ ,
$λ$ denote the ratio of $\tilde{σ}$ and $σ$ with $λ = \frac{\tilde{σ}}{σ}$ .

Peirce's method flags

y_{i}

as a potential outlier if

|y_{i} - μ| \geq x

, where

x = σ^{2} z

and

z

is obtained from the solution of

R^{m} = λ^{m - n} \frac{m^{m} {(n - m)}^{n - m}}{n^{n}}

(1)

where

R = 2 \exp ((\frac{z^{2} - 1}{2}) (1 - Φ (z)))

(2)

and

Φ

is the cumulative distribution function for the standard Normal distribution.

{\tilde{σ}}^{2}

is unknown an assumption is made that the relationship between

{\tilde{σ}}^{2}

and

σ^{2}

, hence

λ

, depends only on the sum of squares of the rejected observations and the ratio estimated as

λ^{2} = \frac{n - p - m z^{2}}{n - p - m}

which gives

z^{2} = 1 + \frac{n - p - m}{m} (1 - λ^{2})

(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

\leq \sqrt{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: $n \geq 3$ .
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: $1 \leq p \leq n - 2$ .
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 $σ^{2}$ , 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 $|y [iout [i - 2] - 1] - μ| \geq |y [iout [i - 1] - 1] - μ|$ , for $i = 2, 3, \dots, n$ .
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 $ldiff \leq 0$ , 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 $|y - μ| - σ^{2} z$ for observation $y [iout [i - 1] - 1]$ , for $i = 1, 2, \dots, \min (ldiff, niout + 1, n - p - 1)$ .
10: llamb[ldiff] – doubleOutput: On exit: if llamb is not NULL then $llamb [i - 1]$ holds $\log (λ^{2})$ for observation $y [iout [i - 1] - 1]$ , for $i = 1, 2, \dots, \min (ldiff, niout + 1, n - p - 1)$ .
11: fail – NagError *Input/Output: The NAG error argument (see Section 3.6 in the Essential Introduction).

6 Error Indicators and Warnings

NE_BAD_PARAM: On entry, argument $⟨value⟩$ had an illegal value.
NE_INT: On entry, $n = ⟨value⟩$ .
Constraint: $n \geq 3$ .
NE_INT_2: On entry, $p = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: $1 \leq p \leq n - 2$ .
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.

7 Accuracy

Not applicable.

8 Parallelism and Performance

Not applicable.

9 Further Comments

One problem with Peirce's algorithm as implemented in nag_outlier_peirce (g07gac) is the assumed relationship between

σ^{2}

, the variance using the full dataset, and

{\tilde{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.

NAG Library Function Documentnag_outlier_peirce (g07gac)

+− Contents

1 Purpose

2 Specification