Let Xi,...,Xn be a random sample from a two parameter exponential distribution with pa- rameter θ...
Consider a random sample of size n from a two-parameter exponential distribution, Xi ~ EXP(\theta ,\eta). Recall from Exercise12 that X1:n and \bar{X} are jointly sufficient for \theta and \eta . (Exercise12: Let X1, . . . , Xn be a random sample from a two-parameter exponential distribution, Xi ~ EXP(\theta ,\eta). Show that X1:n and \bar{X} are jointlly sufficient for \theta and \eta .) Because X1:n is complete and sufficient for \eta for each fixed value of \theta ,...
4. Let Xi, . . . , xn be a random sample from the inverse Gaussian distribution, IG(μ, λ), whose pdf is: (a) Show that the MLE of μ and λ are μ-X and (b) It is known that n)/λ ~X2-1. Use this to derive a 100 . (1-a)% CI for λ.
4. Let Xi, . . . , xn be a random sample from the inverse Gaussian distribution, IG(μ, λ), whose pdf is: (a) Show that the MLE of μ and λ are μ-X and (b) It is known that n)/λ ~X2-1. Use this to derive a 100 . (1-a)% CI for λ.
3. Let Xi, , Xn be a random sample from a Poisson distribution with p.m.f Assume the prior distribution of Of λ is is an exponential with mean 1, i.e. the prior pdi g(A) e-λ, λ > 0 Note that the exponential distribution is a special gamma distribution; and a general gamma distribution with parameters α > 0 and β > 0 has the pd.f. h(A; α, β)-16(. otherwise Also the mean of a gamma random variable with the pd.f.h(Χα,...
4. Let Xi,X2, , Xn be n i.id. exponential random variables with parameter λ > Let X(i) < X(2) < < X(n) be their order statistics. Define Yǐ = nX(1) and Ya = (n +1 - k)(Xh) Xk-n) for 1 < k Sn. Find the joint probability density function of y, . . . , h. Are they independent? 15In
Exercice 6. Let be (Xi,..., Xn) an iid sample from the Bernoulli distribution with parameter θ, ie. I. What is the Maximum Likelihood estimate θ of θ? 2. Show that the maximum likelihood estimator of θ is unbiased. 3. We're looking to cstimate the variance θ (1-9) of Xi . x being the empirical average 2(1-2). Check that T is not unli ator propose an unbiased estimator of θ(1-0).
I. Let X be a random sample from an exponential distribution with unknown rate parameter θ and p.d.f (a) Find the probability of X> 2. (b) Find the moment generating function of X, its mean and variance. (c) Show that if X1 and X2 are two independent random variables with exponential distribution with rate parameter θ, then Y = X1 + 2 is a random variable with a gamma distribution and determine its parameters (you can use the moment generating...
2. (a) Suppose that xi,...,In are a random sample from a gamma distribution with shape parameter and rate parameter λ, Γ(a) Here α > 0 and λ > 0. Let θ sufficient statistic for the data (α, β). Determine the log-likelihood, I(0), and a 2-dimensional b) Suppose that xi,...,In are a random sample from a U(-0,) distribution, 1/(20) if- otherwise x-θ f(x;0)-' 0, Here θ > 0, Determine the likelihood, L(0), and a one-dimensional sufficient statistic. Note that the likelihood...
2. Let Xi, X2, . Xn be a random sample from a distribution with the probability density function f(x; θ-829-1, 0 < x < 1,0 < θ < oo. Find the MLE θ
09 , Let Xi, X2 Xn be a random sample from an exponential distribution with mean 0. (a) Show that a best critical region for testing Ho: 9 3 against Hj:e 5 can be (b) If n-12, use the fact that (2/8) ???, iEX2(24) to find a best critical region of based on the statistic ?, xi . size a 0.1 (12 marks)