CS 536 : Estimation Problems

Uniform Estimators

Let X1, X2, . . . , Xn be i.i.d. random variables, uniformly distributed on [0, L] (i.e., with density 1/L on this interval).

In the posted notes on estimation, it is shown that the method of moments and maximum likelihood estimators for

L are given by

LˆMOM = 2Xn

LˆMLE = max

i=1,…,n

Xi

.

(1)

We want to consider the question of which estimator is better. Recall the definition of the mean squared error of an

estimator as

MSE(Lˆ) = E

Lˆ − L

2

(2)

Note: the answers to homework zero may also be useful here.

1) Show that in general, MSE(ˆθ) = bias(ˆθ)

2 + var(ˆθ), where var is the variance, and bias is given by

bias(ˆθ) = θ − E

h

ˆθ

i

. (3)

2) Show that LˆMOM is unbiased, but that LˆMLE has bias. In general, LˆMLE consistently underestimates L – why?

3) Compute the variance of LˆMOM and LˆMLE.

4) Which one is the better estimator, i.e., which one has the smaller mean squared error?

5) Experimentally verify your computations in the following way: Taking n = 100 and L = 10,

– For j = 1, . . . , 1000:

– Simulate X

j

1

, . . . , Xj

n and compute values for Lˆj

MOM and Lˆj

MLE

– For n = 100, L = 10, simulate X1, . . . , Xn, and compute values for LˆMOM and Lˆj

MLE.

– Estimate the mean squared error for each population of estimator values.

– How do these estimated MSEs compare to your theoretical MSEs?

6) You should have shown that LˆMLE, while biased, has a smaller error over all. Why? The mathematical

justification for it is above, but is there an explanation for this?

7) Find P

LˆMLE < L −

as a function of L, , n. Estimate how many samples I would need to be sure that my

estimate was within with probability at least δ.

8) Show that

Lˆ =

n

n − 1

max

i=1,…,n

Xi

, (4)

is an unbiased estimator, and has a smaller MSE still.

1

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# Estimation Problems- Uniform Estimators

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