g07ab: FL CL CPP AD PY MB

NAG CL Interface
g07abc (ci_poisson)

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Interfaces: FL CL CPP AD PY MB

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g07ab: FL CL CPP AD PY MB

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

▸▿ 10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

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1 Purpose

g07abc computes a confidence interval for the mean parameter of the Poisson distribution.

2 Specification

#include <nag.h>

void	g07abc (Integer n, double xmean, double clevel, double tl, double tu, NagError *fail)

The function may be called by the names: g07abc, nag_univar_ci_poisson or nag_poisson_ci.

3 Description

Given a random sample of size

n

, denoted by

x_{1}, x_{2}, \dots, x_{n}

, from a Poisson distribution with probability function

p (x) = e^{- θ} \frac{θ^{x}}{x!}, x = 0, 1, 2, \dots

the point estimate,

\hat{θ}

, for

θ

is the sample mean,

\bar{x}

Given

n

and

\bar{x}

this function computes a

100 (1 - α) %

confidence interval for the parameter

θ

, denoted by [

θ_{l}, θ_{u}

], where

α

is in the interval

(0, 1)

The lower and upper confidence limits are estimated by the solutions to the equations

\begin{matrix} e^{- n θ_{l}} \sum_{x = T}^{\infty} \frac{{(n θ_{l})}^{x}}{x!} = \frac{α}{2}, \\ e^{- n θ_{u}} \sum_{x = 0}^{T} \frac{{(n θ_{u})}^{x}}{x!} = \frac{α}{2}, \end{matrix}

where

T = \sum_{i = 1}^{n} x_{i} = n \hat{θ}

The relationship between the Poisson distribution and the

χ^{2}

-distribution (see page 112 of Hastings and Peacock (1975)) is used to derive the equations

\begin{array}{l} θ_{l} = \frac{1}{2 n} χ_{2 T, α / 2}^{2}, \\ θ_{u} = \frac{1}{2 n} χ_{2 T + 2, 1 - α / 2}^{2}, \end{array}

where

χ_{ν, p}^{2}

is the deviate associated with the lower tail probability

p

of the

χ^{2}

-distribution with

ν

degrees of freedom.

In turn the relationship between the

χ^{2}

-distribution and the gamma distribution (see page 70 of Hastings and Peacock (1975)) yields the following equivalent equations;

\begin{array}{l} θ_{l} = \frac{1}{2 n} γ_{T, 2; α / 2}, \\ θ_{u} = \frac{1}{2 n} γ_{T + 1, 2; 1 - α / 2}, \end{array}

where

γ_{α, β; δ}

is the deviate associated with the lower tail probability,

δ

, of the gamma distribution with shape parameter

α

and scale parameter

β

. These deviates are computed using g01ffc.

4 References

Hastings N A J and Peacock J B (1975) Statistical Distributions Butterworth

Snedecor G W and Cochran W G (1967) Statistical Methods Iowa State University Press

5 Arguments

1: $n$ – Integer Input: On entry: $n$ , the sample size.

Constraint: $n \geq 1$ .
2: $xmean$ – double Input: On entry: the sample mean, $\bar{x}$ .

Constraint: $xmean \geq 0.0$ .
3: $clevel$ – double Input: On entry: the confidence level, $(1 - α)$ , for two-sided interval estimate. For example $clevel = 0.95$ gives a $95 %$ confidence interval.

Constraint: $0.0 < clevel < 1.0$ .
4: $tl$ – double * Output: On exit: the lower limit, $θ_{l}$ , of the confidence interval.
5: $tu$ – double * Output: On exit: the upper limit, $θ_{u}$ , of the confidence interval.
6: $fail$ – 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_CONVERGENCE: When using the relationship with the gamma distribution the series to calculate the gamma probabilities has failed to converge. Both tl and tu are set to zero. This is an unlikely error exit.
NE_INT: On entry, $n = ⟨ value ⟩$ .
Constraint: $n > 0$ .
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_REAL: On entry, $clevel = ⟨ value ⟩$ .
Constraint: $0.0 < clevel < 1.0$ .

On entry, $xmean = ⟨ value ⟩$ .
Constraint: $xmean \geq 0.0$ .

7 Accuracy

For most cases the results should have a relative accuracy of

\max (0.5e - 12, 50.0 \times ε)

where

ε

is the machine precision (see X02AJC). Thus on machines with sufficiently high precision the results should be accurate to

12

significant digits. Some accuracy may be lost when

α / 2

1 - α / 2

is very close to

0.0

, which will occur if clevel is very close to

1.0

. This should not affect the usual confidence intervals used.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

g07abc is not threaded in any implementation.

9 Further Comments

None.

10 Example

The following example reads in data showing the number of noxious weed seeds and the frequency with which that number occurred in

98

subsamples of meadow grass. The data is taken from page 224 of Snedecor and Cochran (1967). The sample mean is computed as the point estimate of the Poisson parameter

θ

. g07abc is then called to compute both a 95% and a 99% confidence interval for the parameter

θ

g07ab: FL CL CPP AD PY MB

NAG CL Interfaceg07abc (ci_​poisson)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG CL Interface
g07abc (ci_poisson)