7 7. Let Xi, . . . , xn be iid based on f(x:0) = 2x e-x2/0 where x > 0, Show that θ =「X 2 is 2-1 efficient.
7. Let X1,... , Xn be iid based on f(x; 6) -22e-z?/e where x > 0. Show that θ=-yx? is efficient
Suppose that Xi, X2, ..., Xn is an iid sample from where θ > 0. (a) Show that is a complete and sufficient statistic for σ (b) Prove that Y1-X11 follows an exponential distribution with mean σ (c) Find the uniformly minimum variance unbiased estimator (UMVUE) of T(o-o", where r is a fixed constant larger than 0.
Suppose that Xi, X2, ..., Xn is an iid sample from the distribution with density where θ > 0. (c) Show that there is an appropriate statistic T T(X) that has monotone likelihood ratio. (d) Derive the uniformly most powerful (UMP) level α test for
1 Let X1,..., Xn be iid with PDF x/e f(x;0) ',X>0 o (a) Find the method of moments estimator of e. (b) Find the maximum likelihood estimator of O (c) Is the maximum likelihood estimator of efficient?
, xn is an iid sample from fx(x10)-θe-8z1(x > 0), where θ > 0. Suppose X1, X2, For n 2 2, n- is the uniformly minimum variance unbiased estimator (UMVUE) of 0 (d) For this part only, suppose that n-1. If T(Xi) is an unbiased estimator of e, show that Pe(T(X) 0)>0
Suppose that Xi, X2,..., Xn is an iid sample from r > 0 where θ 0. Consider testing Ho : θ-Bo versus H1: θ (a) Derive a size α likelihood ratio test (LRT). (b) Derive the power function P(0) of the LRT. θο, where θο is known. (c) Now consider putting an inverse gamma prior distribution on θ, namely, 1 00), a 4a where a and b are known. Show how to carry out the Bayesian test (d) Is the...
2. Let X 1, , Xn be iid from the distribution modeled by 8-2 fx (1:0)-(9. θ):r"-"(1-2) dr where 0 < x < 1 and θ > 1 Find the MME (method of moments estimate/estimator) for 0
Suppose that Xi, X2,..., Xn are independent random variables (not iid) with densities x, (x^, where 6, > 0, for i-1, 2, , n. versus H1: not Ho (c) Suppose Ho is true so that the common distribution of X1, X2,..., Xn, now viewed as being conditional on 6, is described by where θ > 0. Identify a conjugate prior for 0. Specify any hyperparameters in your prior (pick values for fun if you want). Show how to carry out...
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.