Download The Calculation of Average Run Length for a Nonparametric CUSUM Procedure

The Calculation of Average Run Length
for a Nonparametric CUSUM Procedure
Dennis W. King, STATKING Consulting Inc.
Michael T. Longnecker, Texas A&M U niwrsity
ABSTRACT
terms this means monitoring the mean of the
process random variables.
Many authors have discussed sequential
statistical tests to indicate when a process
goes "out of control", i.e., when the process
mean drifts away from 00 • Some of the authors who have addressed this topic are Shewhart(1931), Page(1954) and Lucas(1976).
All of these articles center on the calculation
of
A method of calculating the probability
mass function of the Wilcoxon signed rank
statistic is discussed. The algorithm presented, written using PROC IML, is based
on the work of Milton (1970). A brief summary of a newly developed error bound for
this algorithm is given.
For process control schemes based on
the Wilcoxon signed rank statistic, the calculation of the Awrage Run Length (ARL)
of the scheme requires the evaluation of the
probability mass function of the Wilcoxon
statistic under a shift in the location parameter. The application of the algorithm's output to the calculation of ARL is shown in detail.
n
Sn =
L
W
n -;· (v(X;) - k),
(Ll)
;=1
at each time point n in the process. The
components of this sum are Wn-j, the weight
placed on the observation at each time point;
v(Xj), some condensation of the data vector
Xj (Xii, ... ,Xgj) observed at time point i;
and k, a reference value. For a CUSUM procedure, we set all Wj=1. The choice of v (Xj)
is arbitrary but a common choice is X - 00 •
We test Ho : 0 = 00 and reject Ho in favor of
Ha : 0 > 00 when Sj ~ h where
=
1. INTRODUCTION
The use of statistical procedures to monitor repetitive manufacturing processes has
become quite widespread. A characteristic
of the process is observed over time. The observed random variables may be measurements of the weight of rolled coils of processed steel or the measured dimension of a
die which was cut to a specified length.
The main thrust is to track whether the
manufactured product is near a management
specified goal value, say 00 • In statistical
Sn = max(O, Sn-l
+ v(Xn) -
k),
(1.2)
where So = o. The reference value k is
chosen so that the decision interval h does
not depend on the time point, i.e., we have
a constant rejection region. Note that we
are making an unknown number of statistical tests since the proced ure continues indefinitely until we reject Ho. The random
variable N will be used to denote the number of tests performed before the process is
487
signaled out of control. We say that E(N),
the expected wlue of N, is the average run
length(ARL) of the procedure. Thisvalue
E(N) is the common "yardstick" by which
we measure one type of charting procedure
versus another.
When the condensation statistic, IJ(X.. )
is a continuous random variable, such as
X - 00 , then the ARL is given by an integral equation (see Lucas(1976)) which must
be solved by methods of numeric integration.
When v(X.. ) is a discrete random variable,
Brook and Evans(1972) have shown that the
ARL is given by
E(N) = (I - p)-l . 1
particular step are the values of W, O,I, ... ,h.
So then the transient state probabilities are
Pio = P(Si ~ 0) = P(W < k - i)
Pij = P(Si = i) = P(W = k + i-i)
Pih = P(Si ~ h) = P(W ~ k + h - i)
(2.2)
To obtain the distribution of W, we first find
the distribution of W+ = E~=I '11 ja(Rn
where a(j) are as in (2.1) andWi(t) = 0,1 as
t <,~ O. Then applying the transformation
W = 2(W+ - E(W+)) = 2W+ - g(g + 1)/2,
P(W = w) = P(W+ = w/2 + E(W+)).
(2.3)
(1.3)
When the process is in control, i.e.
II = 00, all orderings of the ranks are equally
likely and it can be shown (see Randles and
Wolfe(1979),p.52) that
where P is the probability transition matrix
of a Markov chain whose states are the values of the discrete CUSUM.
(2.4)
2. THE DISTRIBUTION OF
THE WILCOXON STATISTIC
where dg(e) is the number of subsets of the
integers (1, ... ,g) for which the sum of the
elements in the subset equals c and c =
0,1, ... , g(g + 1)/2.
When the process is out of control (II >
00), not all permutations of the ranks are
equally likely and additional notation is necessary. Without loss of generality take 110=0.
Let X(I) ~ X(2) ~ ... ~ X(g) be the ordered
values of X"X2, ... , Xg. Denote Zgi = 1
ifthe ithsmallest obserVlltion, X(i) , is non
negative and 0 if the ith smallest IS negative. The rank configurations are then Zg =
(ZgJ, Zg2,""Zgg). Thus, W+ = EiZgj.
Then P(W+ = c) is obtained by summing
Po (Zg = Zg) for all possible rank configurations Zg for which E iZgi = c. For example, with g=5, Pq(W+ = 12) = Pq(Z. =
When g > 1, we may use in place of X00 in (1.1),
9
IJ(Xi) =SRi= EWia(Rt)
(2.1)
j=1
where Rt is the rank of IXj - 00 1among
IXI - Ool,···,IXg - 00 1, Wi = W(Xj -(10)
andw(t) = -1,1 as t <, ~ O. For the set
of scores, aO) j, j=I, ... ,g, the signed linear
rank statistic in (2.1) becomes the Wilcoxon
Signed Rank Statistic, denoted henceforth as
W.
Since this condensation statistic is discrete, the ARL for this type of CUSUM will
be given exactJy by (1.3). The elements of
the probability transition matrix in (1.3) will
be formed from the probability distribution
of W. The states of the Markov chain for any
488
00111) + PO(Z5 = 11011). Klotz(1963) has
shown the expression for PO(Zg = Zg) is
g!
1 1t'-1 1t2 II
00
o
9
•.•
0
0
error introduced by using a Newton-Cotes
formula to approximate the integral over the
finite region will be denoted "eale. The total
error is then
lo(tj - 8jlJ)dtj (2.&)
j=1
{=
where 10 is the pdf of Fo and 8j = 2zj - 1.
Denote this integral as I g •
Milton(1970) recognized that the region
of integration, depicted in Figure 1 in the appendix, for this particular problem allows a
convenient approximation formula. In Figure 1, we see that if we use a simple midpoint
formula for numeric integration in anyone
dimension the region we need will be given
by
12 ..:.
m2(/11121 + 112122 + '13123
2
m2(?= h~{2j +
0=1
.L;
(2.8)
then to ~ ttrune. Once a has been determined, we then select m, the width of the interval in the midpoint numerical integration
procedure, by iterating on m until
(2.6)
«(/maz(m)
where m=(b-a)/3 for M=3 subintervals and
Iii = the pdf 10 in (2.&) evaluated at the
midpoint of its respective subinterval. This
generalizes to M subintervals in 2 dimensions
as
12 •
+ {eale.
When using the algorithm we first specify a
truncation error, say to. It has been shown
by King and Longnecker(1990), that if we
choose the truncation boundary, a, as shown
in Figure 1, such that
+ 111/22 + '11123 + '12123)
M
{trune
+ D(m))9 - (/maz{m»9)
* N ADDS < {eale
(2.10)
where NADDS, the total number of additions performed, -=- (M+:-l), where M =
aim. The value Imaz(m) is height ofthe
highest rectangle in the intergration region
and D (m) is the maximum value of the integration error over the region. These values
are given in King and Longnecker(1990) for
specific symmetric distributions. H we follow this two-step procedure then the total
error will be ~ i. The above error bounding
procedure has been implemented in the SAS
Macros to be described below.
hil2i)
1:0;0 <rSM
(2.7)
We can then further generalize to g dimensions and state the above in matrix terms.
This allows us to program the algorithm using PROC IML. This method is much more
easily programmed than the quadrature
methods and requires only M*g storage locations in the computer.
A brief description of the error bounding for this algorithm is now given. For the
details, see King and Longnecker(1990). The
method of numeric integration used here is
subject to two sources of error. The error introduced by truncating an infinite region of
integration will be denoted by "trune. The
3. SAS MACROS FOR
WILCOXON CUSUM
The programming necessary to implement the procedures discussed in section 2 is
threefold: (a) calculate a and m necessary
to achieve the specified error bound, €, for
489
V(Xi)' under a shift o. The ARL's can then
each probability in the probability density
function of W for the given underlying distribution and group size, (b) compute the probability density function of W under specified shift according to the Milton Algorithm
and (c) calculate the ARL of the Wilcoxon
CUSUM using the pdf of W. This is done
with three SAS Macros, whose call statments
are of the form
be compared to a comparable CUSUM procedure (in terms of in-control ARL) where
V(Xi) = X - 00.
%BOUND(P=,SHIFT=,DIST=,DF=);
%WALTDIST(P=,SHIFT=,DIST=,DF= );
%ARLWILC(P=,SHIFT=,DIST=,DF=,H=,K=
Table 1 shows that when the data comes
from very heavy tailed distributions the ARL
is shorter for the Wilcoxon CUSUM than the
parametric CUSUM. For the other distributions, the loss in ARL is small so the nonparametric procedure can be considered a
global procedure to protect against non Normal data in the small sample case.
5. A DATA EXAMPLE
where
DIST=name of the underlying distribution of the data (NORM, LOGS, DEXP,
As an example, consider data generated
from the soft drink industry. At the end of
each hour of production, five bottles of soft
drink are sampled and their mllevels are
recorded. The data values shown in Table
2 are the deviations of the fill levels relative
to the target value of 8 ounces. The data are
assumed to be sampled from a Normal universe with iT = 1 ounce. An upward shift in
the process mean of .25 ounces has occured.
As we can see, the non parametric CUSUM
detects this shift at time point 5 and the
parametric procedure signals slightly more
quickly at time point 4. This is consistent
with the ARL results of Table 1. The calculations necessary to implement the nonparametric scheme are not that difficult and
could be carried out by technicians or line
personnel.
T)
P=sample size at each time point
H=decision interval
K=reference value
DF=degrees of freedom for the T distribution
SHIFT= the shift from the target mean
in standard deviation units
Note that you may run only the error
bounding macro or the error bound and the
distribution of the Wilcoxon Statistic or all
3 macros. The outputs for these macros are
shown in the appendix.
4. ARL COMPARISONS
The ARL under various shifts of size
Table 1 in the
appendix shows the ARL for a particular
Wilcoxon CUSUM for various underlying
distributions. The errors in using the estimated probabilities from above is < 1%.
Note the underlying distribution must be
specified when using non parametric statistic,
o can then be calculated.
6. DISCUSSION
The procedure discussed above can calculate the values of the probability density
function of the Wilcoxon signed rank statistic to 4 decimal place accuracy for most symmetric densities. It can also calculate the
490
Journal of Quality Technology, 8,
1-12.
ARL for the Wilcoxon CUSUM procedure
described above. However, the code does
have some limitations.
The procedure is inadequate for the
scaled T distribution with ~ 1 degree of freedom. This is due to the fact that the step
size, m, cannot be varied across the region
of integration. This, in turn, males the computer storage and cpu requirements in this
situation infeasible, at least for implementation on most microcomputers.
For other symmetric distributions, the
time necessary to calculate the ARL of the
Wilcoxon CUSUM still prohibits generation
of large tables of ARL. Thus, the procedure
is more of a research tool which allows comparison of the ARL's for this procedure versus ARL's for parametric approaches.
Milton, R.C.(1970). Rank Order Probabilities: Two Sample Normal Shift
Alternati6eB, John Wiley & Sons,
New York, NY.
Page, E.S. (1954). "Continous Inspection Schemes", Biometrika, 41,
100-114.
Randles, R.H. and Wolfe, D.A.(1979).
Introduction to the Theory of Nonparametric Statistics, New York:
John Wiley & Sons.
Shewhart, W.A.(1931). Economic Control of Quality 0/ Manufactured
Product, New York: Van Nostrand.
REFERENCES
Brook,D. and Evans, D.A.(1972). "An
Approach to the Probability Distribution of CUSUM Run Length",
Biometrika, 59, 539-549.
van Dobben de Bruyn, D.S. (1968). Cu·
mulati6e Sum TestB: Theory and
Practice, London: Griffin.
King, D.W. and Longnecker, M.T.(1990) .•
"Computing the Distribution of
the Wilcoxon Statistic with Applicaitons to Process Control", to appear in Communications in Statis·
tics - Simulation and Computing.
Klotz, J.H.(1963). "Small Sample Power
and Efficiency for the One Sample Wilcoxon and Normal Scores
Tests", Annals of Mathematical
StatisticB, 34, 624-632.
Lucas, J.M.(1976). "The Design and
Use of V-Mask Control Schemes",
491
Table 1. Comparison of Parametric and Nonparametric Exact ARL's
for Various Underlying Distributions, g = 5
shift
Procedure
Distri bution
0
.25
.5
1.0
2.0
3.0
CUSUM
Wilcoxon
Normal
Normal
100.9
100.9
30.6
36.1
15.8
17.9
7.9
8.8
5.1
5.5
5.0
5.0
CUSUM
Wilcoxon
Double Exp.
Double Exp.
100.5
100.9
31.0
27.3
15.5
14.1
8.5
8.0
5.0
5.7
5.0
5.2
CUSUM
Wilcoxon
Logistic
Logistic
100.5
100.9
30.0
33.2
16.0
16.4
8.5
8.4
5.0
5.6
5.0
5.1
CUSUM
·Wilcoxon
T* 2 df
T* 2 df
99.5
100.9
42.0
26.0
19.0
13.3
8.5
7.8
5.0
5.8
5.0
5.4
FIG. 1 Region ofIntegration for the Milton Algorithm
X
2
truncation
reg ion
C
'23
o
•
m
•
~--~~--~~--~~~---X1
o
'11
'12
492
'13 C
TABLE 2
Sample Data
-2,-.5,1.1,0.8,0.4
0.2,0.1,1.4,0.0,-1
1.7,2.1,-1,1.1,0.2
-1,0.2,0.2,1.9,1.2
1.7,-.0,0.5,-.3,1.1
Signed Ranks
Wilcoxon
Statistic
-5,-2,4,3,1
3,2,5,1,-4
4,5,-3,2,1
-4,2,1,5,3
5,-1,3,-2,4
Parametric
Wilcoxon
CUSUM
CUSUM
H=.89,K=.129 H=11,K=5
1
7
9
7
9
0.00000
0.00000
0.66293
0.95244 *
1.43964 *
°62
8
12
Output from BOUND Macro
g
SHIFT
DIST
DF
5
1
NORM
4
C
m
EPSCALC
5.265
0.011
.00001
EPSTRUNC
.00001
Output from WALTDIST Macro
OBS
c
1
2
3
1
2
4
3
5
6
4
5
6
7
8
9
10
11
12
13
14
15
Wilcoxon
pdf
0.00010
0.00013
0.00018
0.00054
0.00089
0.00287
0.00419
0.00671
0.01319
0.02571
0.04143
0.05509
0.11568
0.10470
0.20697
0.42155
°
7
8
9
10
11
12
13
14
15
16
Output from ARLWILC Macro
OBS
ARL
1
8.81426
493
NN
479
•