+1,20] distribution, where , X.) 3. Let Xi,...,X be a random sample from Uniform θ >...
3. Let X X be a random sample from Uniform[0, where > 0 is unknown. (a) Show that = max{X,X is the MLE of 0. (b) Let the CDF of @ be F(-). Find F(t) for any t e R (c) Find the pdf of 0 Hint: Find the distribution function of Z maxX1,X. The first feu steps will be as follous: F2(2) P(Z) P (maxX, x) ) = P (XS2, X X,) Nert use the fact that Xis are...
4. Let Xi,... . Xn be lid discrete uniform random variables with common pmf θ, with th θ) being {1, 2, . . .). Let T-max(X1, . .. , X e parameter space for (a) Derive the distribution of T. (Hint: use the edf approach). (b) Give the conditional distribution of Xi,... ,Xn given T-
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...
Let Xi,...,Xn be a random sample from a two parameter exponential distribution with pa- rameter θ (λ, μ), (a) Show that the distribution of Ti = log(X(n)-X) +log λ is free of θ. Îs an ancillary statistics (b) show that 72- Xu is ancillary X-X Let Xi,...,Xn be a random sample from a two parameter exponential distribution with pa- rameter θ (λ, μ), (a) Show that the distribution of Ti = log(X(n)-X) +log λ is free of θ. Îs an...
Problem 2.1. Let Y1, ...,Yn be a random sample from a uniform distribution on the interval [0 – 1,20 + 1]. a. Find the density function of X = Y;-0 (note that Yi ~ Uf0 - 1,20 + 1]). b. Find the density function of Y(n) = max{Y;, i = 1,...,} c. Find a moment estimator of . d. Use the following data to obtain a moment estimate for 4: 11.72 12.81 12.09 13.47 12.37.
5. Let Xi, , X, (n 3) be iid Bernoulli random variables with parameter θ with 0<θ<1. Let T = Σ_iXi and 0 otherwiase. (a) Derive Eo[6(X,, X.)]. (b) Derive Ee16(X, . . . , Xn)IT = t], for t = 0, i, . . . , n.
2. (20pts) Let Xi,..., X be a random sample from a population with pdf f(x)--(1 , where θ > 0 and x > 1. (a) Carry out the likelihood ratio tests of Ho : θ-a, versus Hi : θ a-show that the likelihod ratio statistic corresponding to this test, A, can be re-written as Λ = cYne-ouY, where Y Σ:.. In (X), and the constant c depends on n and θο but not on Y. (b) Make a sketch of...
Let Xi,... , Xn be a random sample from a normal random variable X with E(X) 0 and var(X)-0, i.e., X ~N(0,0) (a) What is the pdf of X? (b) Find the likelihood function, L(0), and the log-likelihood function, e(0) (c) Find the maximun likelihood estimator of θ, θ (d) Is θ unbiased?
3. Let Xi,... , X,n be a random sample from a population with pdf 0, otherwise, where θ > 0. a) Find the method of moments estimator of θ. (b) Find the MLE θ of θ (c) Find the pdf of θ in (b).
2. Let Yı, ..., Yn be a random sample from an Exponential distribution with density function e-, y > 0. Let Y(1) minimum(Yi, , Yn). (a) Find the CDE of Y) b) Find the PDF of Y (c) Is θ-Yu) is an unbiased estimator of θ? Show your work. (d) what modification can be made to θ so it's unbiased? Explain.