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Stat 61 - Homework #7 (11/5/07, due 11/12/07)
1. Estimating a Proportion. Each time we operate this machine, it either works
(success) or it doesn’t (failure). Each attempt is independent. We want to
estimate the single-trial success probability p.
Our data are the results of n trials. Of these, k are hits. One could think of the
data as the values of n independent random variables X1, X2, …, Xn, each
having a Bernoulli (zero-one) distribution with parameter p.
If actual data helps, use n = 100 and k = 30. But I don’t remember the sequence
of successes and failures, so I can’t tell you the observed values of the Xi’s.
a. Express this as a method-of-moments problem. That is, express p as an
unknown parameter of some probability distribution, and compare the theoretical
mean of the distribution to the observed mean of the data. What is the method-ofmoments estimator for p ?
b. Set this up, somehow, as a least-squares problem. (Maybe this way: If you
estimate p, you’re saying that all of the Xi’s “should” have value p. But they
actually have values of 0 and 1. Treat the differences as errors.) What is the
least-squares estimator for p ?
c. What is the maximum-likelihood estimator for p ?
d. Do any of your estimators depend on the (forgotten) sequence of the hits and
misses?
NOTE: Some of you, observing a coin that produced 3 heads in 5 flips, insisted
on estimating 0.50 instead of 0.60 for the coin’s long-term rate of heads.
Apparently you were rejecting method-of-moments, least squares, and maximum
likelihood all at once! So there must be more to estimation than these three
approaches.
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2. Least Squares. Engineering students take 3 measurements of the distance between
two benchmarks on the Swarthmore campus. In meters, the measurements are:
75.005
75.011
75.014
The parameter we care about is D, the true distance.
Our model for the measurements is that each measurement yi is given by
yi = D + ei
where ei’s are independent measurement errors. From experience with other
engineering students (and other benchmarks) we know that measurement errors
are normally distributed with mean 0 and standard deviation 0.009, so their
density function is
fe(e) = constant  e
1 e 
 

2  0.009 
2
.
(1)
Equivalently, the measurements themselves are normal with mean D and
=0.009, giving density
fy(y) = constant  e
1  yD 
 

2  0.009 
2
.
(2)
(Note that (1) and (2) are really the same model, looked at in slightly different
ways. The density for the measurements depends on D.)
Note that if you make an estimate for D, you’re also implicitly estimating the
values of all the measurement errors. For example, if your estimate is D=75.0,
you’re implying that the measurement errors are, respectively,
+0.005, +0.011, +0.014.
The measurement errors are unknown parameters too, and in this example,
knowing one of the parameters means knowing all of them.
a. The least-squares estimator for D is the estimate that minimizes the sum of
the squares of the implied measurement errors. That is, choose D to minimize
E = E(D) = (y1-D)2 + (y2-D)2 + (y3-D)2 .
(3)
What is the least-squares estimator for D ? What is the estimate in this case?
(The estimator should be a function of y1, y2, y3. The estimate should be a
number.)
b. What is the maximum-likelihood estimator for D ?
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3. Weighted least squares. Some people like to use “weighted least squares,” which in
the context of the previous problem means minimizing
E = w1 (y1-D)2 + w2 (y2-D)2 + w3 (y3-D)2
(4)
instead of (3) above. Here the weights are all positive, and somehow express the
importance of the various measurements. (The sum of the wi’s doesn’t matter.)
What is the weighted least squares estimator for D? (Your answer will be a
function of the wi’s and the yi’s.)
4. Different instruments. Again, assume the situation of problem 2. But this time, the
third measurement was made with a better instrument, so it has a standard
deviation of 0.006 instead of 0.009.
a. Write the revised likelihood function.
b. Now, what is the maximum-likelihood estimator for D ?
c. If this can be expressed as a weighted least-squares estimator, what are the
weights? How do they depend on the standard deviations of the measurements?
5. Estimating the parameters of a uniform distribution. Assume that  > . A
random variable Y has a uniform distribution with limits  and . That is, it has
a density function given by
fY(y) = 1/(-) when   x  
(zero otherwise).


This distribution has mean  = ( + )/2 and standard deviation ( –  )/ 12
(which means that the variance is ( – )2/12 ).
Suppose we make random draws y1, y2, y3, y4 from this distribution. Assume that
these have been rearranged so that y1  y2  y3  y4. If you like, assume that they
are:
4.9, 8.2, 9.1, 10.6.
a. Think of a reasonable estimator for the mean, . Try to find one that is
unbiased and has as small a standard error as possible. In any case, say
what you can about this estimator’s properties.
b. Think of a reasonable estimator for the upper limit, . Same goals; say what
you can.
(end)
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