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NAG Library Function Document

nag_rank_ci_2var (g07ebc)

▸▿ 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

1

Purpose

nag_rank_ci_2var (g07ebc) calculates a rank based (nonparametric) estimate and confidence interval for the difference in location between two independent populations.

2

Specification

#include <nag.h>

#include <nagg07.h>

void	nag_rank_ci_2var (Nag_RCIMethod method, Integer n, const double x[], Integer m, const double y[], double clevel, double theta, double thetal, double thetau, double estcl, double ulower, double uupper, NagError *fail)

3

Description

Consider two random samples from two populations which have the same continuous distribution except for a shift in the location. Let the random sample,

x = {(x_{1}, x_{2}, \dots, x_{n})}^{T}

, have distribution

F (x)

and the random sample,

y = {(y_{1}, y_{2}, \dots, y_{m})}^{T}

, have distribution

F (x - θ)

nag_rank_ci_2var (g07ebc) finds a point estimate,

\hat{θ}

, of the difference in location

θ

together with an associated confidence interval. The estimates are based on the ordered differences

y_{j} - x_{i}

. The estimate

\hat{θ}

is defined by

\hat{θ} = median \{y_{j} - x_{i}, i = 1, 2, \dots, n; j = 1, 2, \dots, m\} .

Let

d_{k}

, for

k = 1, 2, \dots, n m

, denote the

n m

(ascendingly) ordered differences

y_{j} - x_{i}

, for

i = 1, 2, \dots, n

and

j = 1, 2, \dots, m

. Then

if $n m$ is odd, $\hat{θ} = d_{k}$ where $k = (n m - 1) / 2$ ;
if $n m$ is even, $\hat{θ} = (d_{k} + d_{k + 1}) / 2$ where $k = n m / 2$ .

This estimator arises from inverting the two sample Mann–Whitney rank test statistic,

U (θ_{0})

, for testing the hypothesis that

θ = θ_{0}

. Thus

U (θ_{0})

is the value of the Mann–Whitney

U

statistic for the two independent samples

\{(x_{i} + θ_{0}), for ​ i = 1, 2, \dots, n\}

and

\{y_{j}, for ​ j = 1, 2, \dots, m\}

. Effectively

U (θ_{0})

is a monotonically increasing step function of

θ_{0}

with

\begin{matrix} mean ​ (U) = μ = \frac{n m}{2}, \\ var (U) = σ^{2} \frac{n m (n + m + 1)}{12} . \end{matrix}

The estimate

\hat{θ}

is the solution to the equation

U (\hat{θ}) = μ

; two methods are available for solving this equation. These methods avoid the computation of all the ordered differences

d_{k}

; this is because for large

n

and

m

both the storage requirements and the computation time would be high.

The first is an exact method based on a set partitioning procedure on the set of all differences

y_{j} - x_{i}

, for

i = 1, 2, \dots, n

and

j = 1, 2, \dots, m

. This is adapted from the algorithm proposed by Monahan (1984) for the computation of the Hodges–Lehmann estimator for a single population.

The second is an iterative algorithm, based on the Illinois method which is a modification of the regula falsi method, see McKean and Ryan (1977). This algorithm has proved suitable for the function

U (θ_{0})

which is asymptotically linear as a function of

θ_{0}

The confidence interval limits are also based on the inversion of the Mann–Whitney test statistic.

Given a desired percentage for the confidence interval,

1 - α

, expressed as a proportion between

0.0

and

1.0

initial estimates of the upper and lower confidence limits for the Mann–Whitney

U

statistic are found;

\begin{matrix} U_{l} = μ - 0.5 + (σ \times Φ^{- 1} (α / 2)) \\ U_{u} = μ + 0.5 + (σ \times Φ^{- 1} ((1 - α) / 2)) \end{matrix}

where

Φ^{- 1}

is the inverse cumulative Normal distribution function.

U_{l}

and

U_{u}

are rounded to the nearest integer values. These estimates are refined using an exact method, without taking ties into account, if

n + m \leq 40

and

\max (n, m) \leq 30

and a Normal approximation otherwise, to find

U_{l}

and

U_{u}

satisfying

\begin{array}{l} P (U \leq U_{l}) \leq α / 2 \\ P (U \leq U_{l} + 1) > α / 2 \end{array}

and

\begin{array}{l} P (U \geq U_{u}) \leq α / 2 \\ P (U \geq U_{u} - 1) > α / 2 . \end{array}

The function

U (θ_{0})

is a monotonically increasing step function. It is the number of times a score in the second sample,

y_{j}

, precedes a score in the first sample,

x_{i} + θ

, where we only count a half if a score in the second sample actually equals a score in the first.

Let

U_{l} = k

; then

θ_{l} = d_{k + 1}

. This is the largest value

θ_{l}

such that

U (θ_{l}) = U_{l}

Let

U_{u} = n m - k

; then

θ_{u} = d_{n m - k}

. This is the smallest value

θ_{u}

such that

U (θ_{u}) = U_{u}

As in the case of

\hat{θ}

, these equations may be solved using either the exact or iterative methods to find the values

θ_{l}

and

θ_{u}

Then

(θ_{l}, θ_{u})

is the confidence interval for

θ

. The confidence interval is thus defined by those values of

θ_{0}

such that the null hypothesis,

θ = θ_{0}

, is not rejected by the Mann–Whitney two sample rank test at the

(100 \times α) %

level.

4

References

Lehmann E L (1975) Nonparametrics: Statistical Methods Based on Ranks Holden–Day

McKean J W and Ryan T A (1977) Algorithm 516: An algorithm for obtaining confidence intervals and point estimates based on ranks in the two-sample location problem ACM Trans. Math. Software 10 183–185

Monahan J F (1984) Algorithm 616: Fast computation of the Hodges–Lehman location estimator ACM Trans. Math. Software 10 265–270

5

Arguments

1: $method$ – Nag_RCIMethodInput

On entry: specifies the method to be used.

$method = Nag_RCI_Exact$: The exact algorithm is used.
$method = Nag_RCI_Approx$: The iterative algorithm is used.

Constraint:

method = Nag_RCI_Exact

Nag_RCI_Approx

2: $n$ – IntegerInput

On entry:

n

, the size of the first sample.

Constraint:

n \geq 1

3: $x [n]$ – const doubleInput

On entry: the observations of the first sample,

x_{i}

, for

i = 1, 2, \dots, n

4: $m$ – IntegerInput

On entry:

m

, the size of the second sample.

Constraint:

m \geq 1

5: $y [m]$ – const doubleInput

On entry: the observations of the second sample,

y_{j}

, for

j = 1, 2, \dots, m

6: $clevel$ – doubleInput

On entry: the confidence interval required,

1 - α

; e.g., for a

95 %

confidence interval set

clevel = 0.95

Constraint:

0.0 < clevel < 1.0

7: $theta$ – double *Output

On exit: the estimate of the difference in the location of the two populations,

\hat{θ}

8: $thetal$ – double *Output

On exit: the estimate of the lower limit of the confidence interval,

θ_{l}

9: $thetau$ – double *Output

On exit: the estimate of the upper limit of the confidence interval,

θ_{u}

10: $estcl$ – double *Output

On exit: an estimate of the actual percentage confidence of the interval found, as a proportion between

(0.0, 1.0)

11: $ulower$ – double *Output

On exit: the value of the Mann–Whitney

U

statistic corresponding to the lower confidence limit,

U_{l}

12: $uupper$ – double *Output

On exit: the value of the Mann–Whitney

U

statistic corresponding to the upper confidence limit,

U_{u}

13: $fail$ – NagError *Input/Output

The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

6

Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
NE_CONVERGENCE: Warning. The iterative procedure to find an estimate of the lower confidence limit has not converged in $100$ iterations.

Warning. The iterative procedure to find an estimate of Theta has not converged in $100$ iterations.

Warning. The iterative procedure to find an estimate of the upper confidence limit has not converged in $100$ iterations.
NE_INT: On entry, $m = 〈value〉$ .
Constraint: $m \geq 1$ .

On entry, $n = 〈value〉$ .
Constraint: $n \geq 1$ .
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 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_NO_LICENCE: 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.
NE_REAL: On entry, clevel is out of range: $clevel = 〈value〉$ .
NE_SAMPLE_IDEN: Not enough information to compute an interval estimate since each sample has identical values. The common difference is returned in theta, thetal and thetau.

7

Accuracy

nag_rank_ci_2var (g07ebc) should return results accurate to five significant figures in the width of the confidence interval, that is the error for any one of the three estimates should be less than

0.00001 \times (thetau - thetal)

8

Parallelism and Performance

nag_rank_ci_2var (g07ebc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

nag_rank_ci_2var (g07ebc) makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.

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

The time taken increases with the sample sizes

n

and

m

10

Example

The following program calculates a 95% confidence interval for the difference in location between the two populations from which the two samples of sizes

50

and

100

are drawn respectively.

NAG Library Function Document

nag_rank_ci_2var (g07ebc)

▸▿ 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

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