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STAT 450

Problems

These problems serve a variety of purposes. One is practice with the ideas from class. Some problems provide counterexamples and illustrate the regularity conditions of theorems. Some problems introduce, without much explanation, ideas we won't have time to consider in class. The first 3 are review intended to let me see how well you explain things you understand and to make sure the basics are already there.

SET A

  1. The concentration of cadmium in a lake is measured 17 times. The measurements average 211 parts per billion with an SD of 15 parts per billion. Could the real concentration of cadmium be below the standard of 200 ppb?.

  2. Consider a population of 200 million people of whom 200 thousand have a certain condition. A test is available with the following properties. Assuming that a person has the condition the probability that the test detects the condition is 0.9. Assuming that a person does not have the condition the test detects (incorrectly) the condition with probability 0.001. A person is picked at random from the 200 million people and the test is administered.

    1. What is the chance that test detects the condition for this randomly selected person?

    2. Assuming that the condition is detected by the test for this randomly selected person what is the chance that the person has the condition?

    3. A mandatory testing program is contemplated. If all 200 million are tested about how many positive results should be expected? Of these about how many will not have the condition?

  3. Suppose X and Y are independent Geometric(p) random variables. In other words

    1. Let , and W=V-U. Express the event U=j and W=k in terms of X and Y.

    2. Compute and and prove that the event U=j and the event W=k are independent.

    3. A computer is waiting for two flags to be set. Both flags start out not set. For each flag the conditional probability that the flag is set next cycle given it is not yet set is 0.5. The two flags operate independently. How many cycles should you expect to wait before both flags are set including the cycle on which the last flag becomes set?

    SET B

  4. Question 27 on page 128.

  5. Suppose are iid real random variables with density f . Let be the X 's arranged in increasing order, that is, X_(1) is the minimum of and while is the maximum.
    1. Find the joint density of .

    2. Suppose . Prove that is independent of .

    3. Find the density of .

    4. Find the density of .

  6. Suppose are iid exponential. Let for i=1, 2 and 3.

    1. Find the joint density of .

    2. Find the joint density of .

    3. Find the joint density of .

  7. Suppose X and Y have joint density . Prove from the definition of density that the density of X is .

  8. Suppose X is Poisson(). After observing X a coin landing Heads with probability p is tossed X times. Let Y be the number of Heads and Z be the number of Tails. Find the joint and marginal distributions of Y and Z.

  9. Let be the bivariate normal density with mean 0, unit variances and correlation and let be the standard bivariate normal density. Let . Show that p has normal margins but is not bivariate normal.

  10. Suppose X and Y are independent with and . Let Z=X+Y. Find the distribution of Z given X and that of X given Z.

    SET C

  11. Number 37 on page 216.

  12. Number 45 on page 216.

  13. Number 48 on page 217.

  14. Number 55 on page 217.

  15. Suppose X and Y are iid .

    1. Show that and are independent.

    2. Show that is uniformly distributed on .

    3. Show is a Cauchy random variable.

    SET E

  16. Suppose are iid and are iid .

    1. Find complete and sufficient statistics.

    2. Find UMVUE's of and .

    3. Now suppose you know that . Find UMVUE's of and of . (You have already found the UMVUE for .)

    4. Now suppose and are unknown but that you know that . Prove there is no UMVUE for . (Hint: Find the UMVUE if you knew with a known. Use the fact that the solution depends on a to finish the proof.)

    5. Why doesn't the Lehmann-Scheffé theorem apply?

  17. Suppose iid Poisson( ). Find the UMVUE for and for .

  18. Suppose iid with

    for . For n=1 and 2 find the UMVUE of . (Hint: The expected value of any function of X is a power series in divided by . Set this equal to and deduce that two power series are equal. Since this implies their coefficients are the same you can see what the estimate must be. )

    SET F

  19. Suppose are independent Poisson() variables. Find the UMP level test of versus and evaluate the constants for the case n=3 and .

  20. Suppose X has a Gamma() distribution with shape parameter known. Find the UMPU test of and evaluate the constants for the case and .

    SET G

  21. Suppose are independent random variables. (This is the usual set-up for the one-way layout.)

    1. Find the MLE's for and .

    2. Find the expectations and variances of these estimators.

  22. Let be the error sum of squares in the ith cell in the previous question.

    1. Find the joint density of the .

    2. Find the best estimate of of the form in the sense of mean squared error.

    3. Do the same under the condition that the estimator must be unbiased.

    4. If only are observed what is the MLE of ?

    5. Find the UMVUE of for the usual one-way layout model, that is, the model of the last two questions.

  23. Exponential families: Suppose are iid with density

    1. Find minimal sufficient statistics.

    2. If are the minimal sufficient statistics show that setting and solving gives the likelihood equations. (Note the connection to the method of moments.)

    SET H

  24. Find the variance stabilizing transformation for the Poisson distribution.

  25. Suppose X, Y and Z are independent standard exponentials. Use numerical Fourier inversion of the characteristic function to compute the density of at 1. You may use the splus function integ.romb (or any other function) found by attaching the directory
    /home/math/lockhart/research/software/quadrature/.Data.

  26. Suppose X is an integer valued random variable. Let be the characteristic function of X. Show that

  27. Suppose are independent random variables such that . Prove that

    where is the standard normal density. You should use the previous problem and Taylor expansion of the characteristic function around 0. Also do the same thing using Sterling's formula.

    SET I

  28. Suppose are iid exponential().

    1. Find the exact confidence levels of 95% intervals based on normal approximations to the distributions of the pivots , , and for n=10, 20 and 40.

    2. Find the shortest exact 95% confidence interval based on ; get numerical values for n=10, 20 and 40.

    3. Find the exact confidence level of 95% confidence intervals based on the chi-squared approximation to the distribution of deviance drop. Compare the results with the previous question based on length and coverage probabilities. Figure out how to make a convincing comparison. Which method is better?

  29. Suppose are iid with density f. Assume the median of f is 0. In this problem you will study the asymptotic distribution of the sample median. To simplify things you may assume that n=2m-1 is odd so that the median is the mth order statistic.

    1. Let M denote the sample median and be the number of X's less than or equal to x. Express the event that in terms of the number .

    2. What is the exact distribution of ?

    3. Show that the expected value, , of can be written as .

    4. Show that is asymptotically normal with mean 0 and variance .

    5. Deduce from all the preceding that is asymptotically normal with mean 0 and variance .

  30. In question 20 take for all i and let . What happens to the MLE of ?

    SET J

  31. Suppose that are independent random variables and that are the corresponding values of some covariate. Suppose that the density of is

    where , and are unknown parameters.

    1. Find the log-likelihood, the score function and the Fisher information.

    2. For the data set in /home/math/lockhart/teaching/801/data1 fit the model and produce a contour plot of the log-likelihood surface, the profile likelihood for and an approximate 95% confidence interval for .

  32. Consider the random effects one way layout. You have data and a model where the 's are iid and the 's are iid .

    1. Write down the likelihood.

    2. Find minimal sufficient statistics.

    3. Are they complete?

    4. Find method of moments estimates of the three parameters.

    5. Can you find the MLE's?

  33. For each of the doses a number of animals are treated with the corresponding dose of some drug. The number dying at dose d is Binomial with parameter . A common model for is

    1. Find the likelihood equations for estimating and .

    2. Find the Fisher information matrix.

    3. Define the parameter LD50 as the value of d for which ; express LD50 as a function of and .

    4. Use a Taylor expansion to find large sample confidence limits for LD50.

    5. At each of the doses -3.204, -2.903, 2.602, -2.301 and -2.000 a sample of 40 mice were exposed to antipneumonococcus serum. The numbers surviving were 7, 18, 32, 35, and 38 respectively. Get numerical values for the theory above. You can use glm or get preliminary estimates based on linear regression of the MLE of against dose.

  34. Suppose are a sample of size n from the density

    In the following question the digamma function is defined by and the trigamma function is the derivative of the digamma function. From the identity you can deduce recurrence relations for the digamma and trigamma functions.

    1. For known find the mle for .

    2. When both and are unknown what equation must be solved to find , the mle of ?

    3. Evaluate the Fisher information matrix.

    4. A sample of size 20 is in the file
      /home/math/lockhart/teaching/801/gamma.
      Use this data in the following questions. First take and find the mle of subject to this restriction.

    5. Now use and to get method of moments estimates and for the parameters.

    6. Do two steps of Newton Raphson to get MLEs.

    7. Use Fisher's scoring idea to redo the previous question.

    8. Compute standard errors for the MLEs and compare the difference between the estimates in the previous 2 questions to the SEs.

    9. Do a likelihood ratio test of .

    SET K

  35. Suppose X is Bin(n,p) with p unknown. Define for b > 0.

    1. Find the risk functions for these estimators.

    2. For the Beta prior distribution on p with and both positive find the posterior distribution of p given X.

    3. Find the Bayes procedures for these priors.

    4. Discuss consistency and asymptotic distribution theory for the Bayes procedures.

    5. Find the minimax procedure.

    6. Compare the risk function of the minimax procedure, the MLE and the estimate for n=1,100 and asymptotically. Provide graphs.

    7. What happens to the posterior distribution of p as n tends to infinity?





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Richard Lockhart
Sat Sep 14 23:03:15 PDT 1996