a) Write down the log likelihood function for the sample and show that the MLE is the sample mean. b) Derive the score and the Hessian (it's a scalar here since there is only one parameter). Derive the information “matrix," which is scalar here. c) Derive the formulas for the Wald and LM tests of the hypothesis that 0= 0o (against the alternative that it's anything else). Verify that under the null the two test statistics have the same probability limit.

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1) Consider an independent sample of size N drawn from the Bernoulli distribution,
with probability parameter 0. That is, yi = 1 with probability 0 and zero with
probability 1- 0.
a) Write down the log likelihood function for the sample and show that the MLE is
the sample mean.
b) Derive the score and the Hessian (it's a scalar here since there is only one
parameter). Derive the information “matrix," which is scalar here.
c) Derive the formulas for the Wald and LM tests of the hypothesis that 0= 0o
(against the alternative that it's anything else). Verify that under the null the two
test statistics have the same probability limit.
d) Derive the LR test statistic for the same hypothesis.
e) Suppose you draw a sample with 5 observations, all of which are zero. Your prior
distribution is uniform. Find the posterior mean and construct a 90% Bayesian
coverage area.
Transcribed Image Text:1) Consider an independent sample of size N drawn from the Bernoulli distribution, with probability parameter 0. That is, yi = 1 with probability 0 and zero with probability 1- 0. a) Write down the log likelihood function for the sample and show that the MLE is the sample mean. b) Derive the score and the Hessian (it's a scalar here since there is only one parameter). Derive the information “matrix," which is scalar here. c) Derive the formulas for the Wald and LM tests of the hypothesis that 0= 0o (against the alternative that it's anything else). Verify that under the null the two test statistics have the same probability limit. d) Derive the LR test statistic for the same hypothesis. e) Suppose you draw a sample with 5 observations, all of which are zero. Your prior distribution is uniform. Find the posterior mean and construct a 90% Bayesian coverage area.
Expert Solution
Step 1

Bernoulli Distribution

Bernoulli distribution is a discrete distribution having two outcomes one is success denoted as p and failure denoted as q = 1-p where 0<p<1

P(n) = 1-p; n =0p      ; n =1

The probability density function is given as 

P(n) = pn (1-p)1-n

Probability mass function of x

f(xi|p) = pxi(1-p)1-xi x = i=1nxiLikelihoodL(p) = i=1npxi(1-p)1-xi=px(1-p)n-x

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