Method of Maximum Likelihood in .NET

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Method of Maximum Likelihood
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One of the best methods of obtaining a point estimator of a parameter is the method of maximum likelihood. This technique was developed in the 1920s by a famous British statistician, Sir R. A. Fisher. As the name implies, the estimator will be the value of the parameter that maximizes the likelihood function.
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7-3 METHODS OF POINT ESTIMATION
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De nition Suppose that X is a random variable with probability distribution f (x; ), where is a single unknown parameter. Let x1, x2, p , xn be the observed values in a random sample of size n. Then the likelihood function of the sample is L1 2 f 1x1; 2 f 1x2; 2 p f 1xn ; 2 (7-5)
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Note that the likelihood function is now a function of only the unknown parameter . The maximum likelihood estimator of is the value of that maximizes the likelihood function L( ).
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In the case of a discrete random variable, the interpretation of the likelihood function is clear. The likelihood function of the sample L( ) is just the probability P1X1 x1, X2 x2, p , Xn xn 2
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That is, L( ) is just the probability of obtaining the sample values x1, x2, p , xn. Therefore, in the discrete case, the maximum likelihood estimator is an estimator that maximizes the probability of occurrence of the sample values. EXAMPLE 7-6 Let X be a Bernoulli random variable. The probability mass function is f 1x; p2 e px 11 0, p2 1 x, x 0, 1 otherwise
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where p is the parameter to be estimated. The likelihood function of a random sample of size n is L1 p2 px1 11
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We observe that if p maximizes L( p), p also maximizes ln L( p). Therefore,
ln L1 p2 Now
a a xi b ln p
a xi b ln 11
d ln L1 p2 dp
a xi
an 1
a xi b
Equating this to zero and solving for p yields p likelihood estimator of p is P
11 n2 g i
xi . Therefore, the maximum
1 n n ia Xi 1
CHAPTER 7 POINT ESTIMATION OF PARAMETERS
Suppose that this estimator was applied to the following situation: n items are selected at random from a production line, and each item is judged as either defective (in which case n we set xi 1) or nondefective (in which case we set xi 0). Then g i 1 xi is the number of defective units in the sample, and p is the sample proportion defective. The parameter p is the population proportion defective; and it seems intuitively quite reasonable to use p as an estimate of p. Although the interpretation of the likelihood function given above is con ned to the discrete random variable case, the method of maximum likelihood can easily be extended to a continuous distribution. We now give two examples of maximum likelihood estimation for continuous distributions.
EXAMPLE 7-7
Let X be normally distributed with unknown and known variance function of a random sample of size n, say X1, X2, p , Xn, is L1 2 Now ln L1 2 and d ln L1 2 d 1
1 12
. The likelihood
22 12 22
2 n2
11 2 22
1xi 1
1n 22 ln12
a 1xi
a 1xi
Equating this last result to zero and solving for
yields
a Xi
Thus the sample mean is the maximum likelihood estimator of . Notice that this is identical to the moment estimator.
EXAMPLE 7-8
Let X be exponentially distributed with parameter . The likelihood function of a random sample of size n, say X1, X2, p , Xn, is L1 2 The log likelihood is ln L1 2
n n n xi n
xi 1
n ln
a xi
7-3 METHODS OF POINT ESTIMATION
Now d ln L1 2 d n
a xi
and upon equating this last result to zero we obtain
n a Xi
Thus the maximum likelihood estimator of this is the same as the moment estimator.
is the reciprocal of the sample mean. Notice that
It is easy to illustrate graphically just how the method of maximum likelihood works. Figure 7-3(a) plots the log of the likelihood function for the exponential parameter from Example 7-8, using the n 8 observations on failure time given following Example 7-3. We 0.0462. From Example 7-8, we know that this is a found that the estimate of was maximum likelihood estimate. Figure 7-3(a) shows clearly that the log likelihood function is maximized at a value of that is approximately equal to 0.0462. Notice that the log likelihood function is relatively at in the region of the maximum. This implies that the parameter is not estimated very precisely. If the parameter were estimated precisely, the log likelihood function would be very peaked at the maximum value. The sample size here is relatively small, and this has led to the imprecision in estimation. This is illustrated in Fig. 7-3(b) where we have plotted the difference in log likelihoods for the maximum value, assuming that the sample sizes were n 8, 20, and 40 but that the sample average time to failure remained constant at x 21.65. Notice how much steeper the log likelihood is for n 20 in comparsion to n 8, and for n 40 in comparison to both smaller sample sizes. The method of maximum likelihood can be used in situations where there are several unknown parameters, say, 1, 2, p , k to estimate. In such cases, the likelihood function is a function of the k unknown parameters 1, 2, p , k, and the maximum likelihood estimators 5 i 6 would be found by equating the k partial derivatives L1 1, 2, p , k 2 i, i 1, 2, p , k to zero and solving the resulting system of equations.