NAG Library Function Document
nag_rank_ci_1var (g07eac)
1
Purpose
nag_rank_ci_1var (g07eac) computes a rank based (nonparametric) estimate and confidence interval for the location argument of a single population.
2
Specification
#include <nag.h> 
#include <nagg07.h> 
void 
nag_rank_ci_1var (Nag_RCIMethod method,
Integer n,
const double x[],
double clevel,
double *theta,
double *thetal,
double *thetau,
double *estcl,
double *wlower,
double *wupper,
NagError *fail) 

3
Description
Consider a vector of independent observations,
$x={\left({x}_{1},{x}_{2},\dots ,{x}_{n}\right)}^{\mathrm{T}}$ with unknown common symmetric density
$f\left({x}_{i}\theta \right)$.
nag_rank_ci_1var (g07eac) computes the Hodges–Lehmann location estimator (see
Lehmann (1975)) of the centre of symmetry
$\theta $, together with an associated confidence interval. The Hodges–Lehmann estimate is defined as
Let
$m=\left(n\left(n+1\right)\right)/2$ and let
${a}_{\mathit{k}}$, for
$\mathit{k}=1,2,\dots ,m$ denote the
$m$ ordered averages
$\left({x}_{i}+{x}_{j}\right)/2$ for
$1\le i\le j\le n$. Then
 if $m$ is odd, $\hat{\theta}={a}_{k}$ where $k=\left(m+1\right)/2$;
 if $m$ is even, $\hat{\theta}=\left({a}_{k}+{a}_{k+1}\right)/2$ where $k=m/2$.
This estimator arises from inverting the onesample Wilcoxon signedrank test statistic,
$W\left(x{\theta}_{0}\right)$, for testing the hypothesis that
$\theta ={\theta}_{0}$. Effectively
$W\left(x{\theta}_{0}\right)$ is a monotonically decreasing step function of
${\theta}_{0}$ with
The estimate
$\hat{\theta}$ is the solution to the equation
$W\left(x\hat{\theta}\right)=\mu $; two methods are available for solving this equation. These methods avoid the computation of all the ordered averages
${a}_{k}$; this is because for large
$n$ both the storage requirements and the computation time would be excessive.
The first is an exact method based on a set partitioning procedure on the set of all ordered averages
$\left({x}_{i}+{x}_{j}\right)/2$ for
$i\le j$. This is based on the algorithm proposed by
Monahan (1984).
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
$W\left(x{\theta}_{0}\right)$ which is asymptotically linear as a function of
${\theta}_{0}$.
The confidence interval limits are also based on the inversion of the Wilcoxon test statistic.
Given a desired percentage for the confidence interval,
$1\alpha $, expressed as a proportion between
$0$ and
$1$, initial estimates for the lower and upper confidence limits of the Wilcoxon statistic are found from
and
where
${\Phi}^{1}$ is the inverse cumulative Normal distribution function.
${W}_{l}$ and
${W}_{u}$ are rounded to the nearest integer values. These estimates are then refined using an exact method if
$n\le 80$, and a Normal approximation otherwise, to find
${W}_{l}$ and
${W}_{u}$ satisfying
and
Let
${W}_{u}=mk$; then
${\theta}_{l}={a}_{k+1}$. This is the largest value
${\theta}_{l}$ such that
$W\left(x{\theta}_{l}\right)={W}_{u}$.
Let ${W}_{l}=k$; then ${\theta}_{u}={a}_{mk}$. This is the smallest value ${\theta}_{u}$ such that $W\left(x{\theta}_{u}\right)={W}_{l}$.
As in the case of $\hat{\theta}$, these equations may be solved using either the exact or the iterative methods to find the values ${\theta}_{l}$ and ${\theta}_{u}$.
Then $\left({\theta}_{l},{\theta}_{u}\right)$ is the confidence interval for $\theta $. The confidence interval is thus defined by those values of ${\theta}_{0}$ such that the null hypothesis, $\theta ={\theta}_{0}$, is not rejected by the Wilcoxon signedrank test at the $\left(100\times \alpha \right)\%$ level.
4
References
Lehmann E L (1975) Nonparametrics: Statistical Methods Based on Ranks Holden–Day
Marazzi A (1987) Subroutines for robust estimation of location and scale in ROBETH Cah. Rech. Doc. IUMSP, No. 3 ROB 1 Institut Universitaire de Médecine Sociale et Préventive, Lausanne
McKean J W and Ryan T A (1977) Algorithm 516: An algorithm for obtaining confidence intervals and point estimates based on ranks in the twosample 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:
$\mathbf{method}$ – Nag_RCIMethodInput

On entry: specifies the method to be used.
 ${\mathbf{method}}=\mathrm{Nag\_RCI\_Exact}$
 The exact algorithm is used.
 ${\mathbf{method}}=\mathrm{Nag\_RCI\_Approx}$
 The iterative algorithm is used.
Constraint:
${\mathbf{method}}=\mathrm{Nag\_RCI\_Exact}$ or $\mathrm{Nag\_RCI\_Approx}$.
 2:
$\mathbf{n}$ – IntegerInput

On entry: $n$, the sample size.
Constraint:
${\mathbf{n}}\ge 2$.
 3:
$\mathbf{x}\left[{\mathbf{n}}\right]$ – const doubleInput

On entry: the sample observations,
${x}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$.
 4:
$\mathbf{clevel}$ – doubleInput

On entry: the confidence interval desired.
For example, for a $95\%$ confidence interval set ${\mathbf{clevel}}=0.95$.
Constraint:
$0.0<{\mathbf{clevel}}<1.0$.
 5:
$\mathbf{theta}$ – double *Output

On exit: the estimate of the location, $\hat{\theta}$.
 6:
$\mathbf{thetal}$ – double *Output

On exit: the estimate of the lower limit of the confidence interval, ${\theta}_{l}$.
 7:
$\mathbf{thetau}$ – double *Output

On exit: the estimate of the upper limit of the confidence interval, ${\theta}_{u}$.
 8:
$\mathbf{estcl}$ – double *Output

On exit: an estimate of the actual percentage confidence of the interval found, as a proportion between $\left(0.0,1.0\right)$.
 9:
$\mathbf{wlower}$ – double *Output

On exit: the upper value of the Wilcoxon test statistic, ${W}_{u}$, corresponding to the lower limit of the confidence interval.
 10:
$\mathbf{wupper}$ – double *Output

On exit: the lower value of the Wilcoxon test statistic, ${W}_{l}$, corresponding to the upper limit of the confidence interval.
 11:
$\mathbf{fail}$ – 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 $\u2329\mathit{\text{value}}\u232a$ had an illegal value.
 NE_CONVERGENCE

Warning. The iterative procedure to find an estimate of the lower confidence point had not converged in $100$ iterations.
Warning. The iterative procedure to find an estimate of Theta had not converged in $100$ iterations.
Warning. The iterative procedure to find an estimate of the upper confidence point had not converged in $100$ iterations.
 NE_INT

On entry, ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{n}}\ge 2$.
 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.
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:
${\mathbf{clevel}}=\u2329\mathit{\text{value}}\u232a$.
 NE_SAMPLE_IDEN

Not enough information to compute an interval estimate since the whole sample is identical. The common value is returned in
theta,
thetal and
thetau.
7
Accuracy
nag_rank_ci_1var (g07eac) should produce 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 $0.00001\times \left({\mathbf{thetau}}{\mathbf{thetal}}\right)$.
8
Parallelism and Performance
nag_rank_ci_1var (g07eac) 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 implementationspecific information.
The time taken increases with the sample size $n$.
10
Example
The following program calculates a 95% confidence interval for $\theta $, a measure of symmetry of the sample of $50$ observations.
10.1
Program Text
Program Text (g07eace.c)
10.2
Program Data
Program Data (g07eace.d)
10.3
Program Results
Program Results (g07eace.r)