random sample of size n from the p.d.f. 1.8 On the basis of a (x,θ)-θΧθ-1 ,...
A random sample of size n = 2 is taken from the p.d.f f(x) = {1 for 0 ≤ x ≤ 1 and 0 otherwise. Find P(X-bar ≥ 0.9) 3. A random sample of size n = 2 is taken from the p.d.f 1 for 0 < x < 1 f(30 0 otherwise. Find P(X > 0.9)
x, R(x), is defined as the probability that X>x; ie, R(x)-P(X> x). Now, suppose that X has the Negative Exponential p.d.f, Then (ii) Use Theorem 1 in order to determine the MLE of R(x; θ), on the basis of a random sample X1,…, X from the underlying p.d.f. Theorem 1 Let θ-θ(x) be the MLE of θ on the basis of the observed values x1, , xn of the random sample X1, , X, from the pdf f(", θ), θ...
l. Find the maxinum likelihood estimator (MLE) of θ based on a random sample X1 , xn fronn each of the following distributions (a) f(x:0)-θ(1-0)z-1 , X-1, 2, . . . . 0 θ < 1
2.6 the function f(x θ)-6x-(θ+1), x 2 lde Ω-(0,0) is a p.d.f. (ii) On the basis of a random sample of size n from this pdf., show that the statistic X, sufficient for θ X is
Let X, X,, ..., X, denote a random sample of size n from a population with pdf (10) = b exp(@m()).0<x<1 where (<O<0. Derive that the likelihood ratio test of H.:0=1 versus H, :0 #1 in terms of T(x) = ŽI (3)
(1 point) Let X1 and X2 be a random sample of size n= 2 from the exponential distribution with p.d.f. f(x) = 4e - 4x 0 < x < 0. Find the following: a) P(0.5 < X1 < 1.1,0.3 < X2 < 1.7) = b) E(X1(X2 – 0.5)2) =
1. Let Xi,..., Xn be a random sample from a distribution with p.d.f. f(x:0)-829-1 , 0 < x < 1. where θ > 0. (a) Find a sufficient statistic Y for θ. (b) Show that the maximum likelihood estimator θ is a function of Y. (c) Determine the Rao-Cramér lower bound for the variance of unbiased estimators 12) Of θ
21 Let x.., X, be ii.d. r.v.s with the Negative Exponential p.d.f., /(x.6)0,ee-(O.) Then , x > 0,9 e Ω-(0,00 (i) Show that 1 / X is the MLE of θ.
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).
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.