In those cases where a probability density for the parameters
cannot be assigned, the model evaluation problem can be solved
in several ways; the methods used depend on the form of the
likelihood ratio and the way in which the parameter(s) enter the
problem. In the Gaussian problem we have discussed so often,
the threshold used in the likelihood ratio test
η
η
may be unity. In this case, examination of the resulting
computations required reveals that implementing the test
does not require knowledge of the variance of the
observations (see this problem). Thus, if the common
variance of the underlying Gaussian distributions is not known,
this lack of knowledge has no effect on the
optimum decision rule. This happy situation  knowledge of the
value of a parameter is not required by the optimum decision
rule  occurs rarely, but should be checked before using more
complicated procedures.
A second fortuitous situation occurs when the sufficient
statistic as well as its probability density under one of the
models do not depend on the unknown
parameter(s). Although the sufficient statistic's threshold
γ
γ
expressed in terms of the likelihood ratio's threshold
η
η
depends on the unknown parameters,
γ
γ
may be computed as a single value using the NeymanPearson
criterion if the computation of the falsealarm
probability does not involve the unknown parameters.
Continuing the example of the previous section, let's consider the
situation where the value of the mean of each observation
under model
ℳ
1
ℳ
1
is not known. The sufficient statistic is the sum of the
observations (that quantity doesn't depend on
m
m)
and the distribution of the observation vector under model
ℳ
0
ℳ
0
does not depend on
m
m
(allowing computation of the falsealarm probability).
However, a subtlety emerges; in the derivation of the
sufficient statistic, we had to divide by the value of the
mean. The critical step occurs once the logarithm of the
likelihood ratio is manipulated to obtain
m∑l=0L−1
r
l
≷
ℳ
0
ℳ
1
(σ2lnη+Lm22)
m
l
0
L
1
r
l
≷
ℳ
0
ℳ
1
σ
2
η
L
m
2
2
Recall that only positively monotonic
transformations can be applied; if a negatively monotonic
operation is applied to this inequality (such as multiplying
both sides by 1), the inequality
reverses. If the sign of
m
m
is known, it can be taken into account explicitly and a
sufficient statistic results. If, however, the sign is not
known, the above expression cannot be manipulated further and
the left side constitutes the sufficient statistic for this
problem. The sufficient statistic then depends on the unknown
parameter and we cannot develop a decision rule in this case.
If the sign is known, we can proceed. Assuming the sign of
m
m
is positive, the sufficient statistic is the sum of the
observations and the threshold
γ
γ
is found by
γ=LσQ1
P
F
γ
L
σ
Q
P
F
Note that if the variance
σ2
σ
2
instead of the mean were unknown, we could not compute the
threshold. The difficulty lies not with the sufficient
statistic (it doesn't depend on the variance), but with the
false alarm probability as the expression indicates. Another
approach is required to deal with the unknownvariance
problem.
When this situation occurs  the sufficient statistic
and the falsealarm probability can be
computed without needing the parameter in question, we have
established what is known as a uniformly most powerful
test (or UMP test) (Cramér;
p.529531), (van Trees;
p.89ff). If an UMP test does not exist, which can only
be demonstrated by explicitly finding the sufficient statistic
and evaluating its probability distribution, then the composite
hypothesis testing problem cannot be solved without some value
for the parameter being used.
This seemingly impossible situation  we need the value of the
parameter that is assumed unknown  can be approached by noting
that some data is available for "guessing" the value of the
parameter. If a reasonable guess could be obtained, it could
then be used in our model evaluation procedures developed in
this chapter. The data available for estimating
unknown parameters are precisely the data used in the decision
rule. Procedures intended to yield "good" guesses of
the value of a parameter are said to be parameter
estimates. Estimation procedures are the topic of
the next chapter; there we will explore a variety of estimation
techniques and develop measure of estimate quality. For the
moment, these issues are secondary; even if we knew the size of
the estimation error, for example, the more pertinent issue is
how the imprecise parameter value affects the performance
probabilities. We can compute these probabilities
without explicitly determining the
estimate's error characteristics.
One parameter estimation procedure that fits nicely into the
composite hypothesis testing problem is the maximum
likelihood estimate. Letting
r
r
denote the vector of observables and
θ
θ
a vector of parameters, the maximum likelihood estimate of
θ
θ,
θ^ML
θ
ML
, is that value of
θ
θ
that maximizes the conditional density
p
r

θ
r
p
r
θ
r
of the observations given the parameter values. To use
θ^ML
θ
ML
in our decision rule, we estimate the parameter vector
separately for each model, use the
estimated value in the conditional density of the observations,
and compute the likelihood ratio. This procedure is termed the
generalized likelihood ratio test for the unknown
parameter problem in hypothesis testing (Lehmann; p.16), (van
Trees; p.92ff).
Λr=max
θ
θ
p
r

ℳ
1
θ
rmax
θ
θ
p
r

ℳ
0
θ
r
Λ
r
θ
p
r
ℳ
1
θ
r
θ
p
r
ℳ
0
θ
r
(1)
Note that we do
not find that value of the
parameter that (necessarily) maximizes the likelihood ratio.
Rather, we estimate the parameter value most consistent with the
observed data in the context of each assumed model (hypothesis)
of data generation. In this way, the estimate conforms with
each potential model rather than being determined by some
amalgam of supposedly mutually exclusive models.
Returning to our Gaussian example, assume that the variance
σ2
σ
2
is known but that the mean under
ℳ
1
ℳ
1
is unknown.
ℳ
0
:
r∼0σ2I
ℳ
0
:
r
0
σ
2
I
ℳ
1
:
r∼mσ2I
ℳ
1
:
r
m
σ
2
I
m=m…m
,
m=?
m
m
…
m
,
m
?
The unknown quantity occurs only in the exponent of the
conditional density under
ℳ
1
ℳ
1
;
to maximize this density, we need only to maximize the
exponent. Thus, we consider the derivative of the exponent
with respect to
m
m.
(∂((−12σ2)∑l=0L−1
r
l
−m2)∂mm=m^ML=0)⇒(∑l=0L−1
r
l
−m^ML=0)
m
m
ML
m
1
2
σ
2
l
0
L
1
r
l
m
2
0
l
0
L
1
r
l
m
ML
0
The solution of this equation is the average value of the
observations
m^ML=1L∑l=0L−1
r
l
m
ML
1
L
l
0
L
1
r
l
To derive the decision rule, we substitute this estimate in
the conditional density for
ℳ
1
ℳ
1
.
The critical term, the exponent of this density, is
manipulated to obtain
(−12σ2)∑l=0L−1
r
l
−1L∑k=0L−1
r
k
2=(−12σ2)(∑l=0L−1
r
l
2
−1L∑l=0L−1
r
l
2)
1
2
σ
2
l
0
L
1
r
l
1
L
k
0
L
1
r
k
2
1
2
σ
2
l
0
L
1
r
l
2
1
L
l
0
L
1
r
l
2
Noting that the first term in this exponent is identical to
the exponent of the denominator in the likelihood ratio, the
generalized likelihood ratio becomes
Λr=e12Lσ2∑l=0L−1
r
l
2
Λ
r
1
2
L
σ
2
l
0
L
1
r
l
2
The sufficient statistic thus becomes the square (or
equivalently the magnitude) of the summed observations.
Compare this result with that obtained in Example 1. There, an UMP test existed
if we knew the sign of
m
m
and the sufficient statistic was the sum of the observations.
Here, where we employed the generalized likelihood ratio test,
we made no such assumptions about
m
m;
this generality accounts for the difference in sufficient
statistic. Which test do you think would lead to a greater
detection probability for a given falsealarm probability?
Once the generalized likelihood ratio is determined, we need to
determine the threshold. If the a priori
probabilities
π
0
π
0
and
π
1
π
1
are known, the evaluation of the threshold proceeds in the usual
way. If they are not known, all of the conditional densities
must not depend on the unknown parameters lest the performance
probabilities also depend upon them. In most cases, the
original model evaluation problem is posed in such a way that
one of the models does not depend on the unknown parameter; a
criterion on the performance probability related to that model
can then be established via the NeymanPearson procedure. If
not the case, the threshold cannot be computed and the threshold
must be set experimentally: we force one of the models to be
true and modify the threshold on the sufficient statistic until
the desired level of performance is reached. Despite this
nonmathematical approach, the overall performance of the model
evaluation procedure will be optimum because of the results
surrounding the NeymanPearson criterion.

H. Cramér. (1946). Mathematical Methods of Statistics. Princeton, NJ: Princeton University Press.

H.L. van Trees. (1968). Detection, Estimation, and Modulation Theory, Part I. New York: John Wiley and Sons.

E.L. Lehmann. (1986). Testing Statistical Hypotheses. (second edition). New York: John Wiley and Sons.