Let XI, X2, , Xn İs a random sample from the probability density function Use factorization...
2. Let Xi, X,.., Xn denote a random sample from the probability density function Show that X(i) = min(X1,X2, . . . , Xn} is sufficient for ?. Hint: use an indictorfunction since the support depends on ?
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
QUESTION 3 17) Let Xi. X. X be a random sample from a distribution with probability density function f(x, ?) | ße_ß, for x >0 elsewhere (a) What is the likelihood (LU) = L (x1.X2. xalß)) of the sample? Simplify it. (b) Use the factorization criterion/theorem to show that ? x, is a sufficient statistic for . 4
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability density function f(x; θ) = (1/2)e-11-01, o < x < oo,-oo < θ < oo. Find the NILE θ.
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability density function f(x; θ) = (1/2)e-11-01, o < x < oo,-oo < θ < oo. Find the NILE θ.
Let X1, . . . , Xn be a random sample from a population with density 8. Let Xi,... ,Xn be a random sample from a population with density 17 J 2.rg2 , if 0<、〈릉 0 , if otherwise ( a) Find the maximum likelihood estimator (MLE) of θ . (b) Find a sufficient statistic for θ (c) Is the above MLE a minimal sufficient statistic? Explain fully.
Let X1, X2,.. Xn be a random sample from a distribution with probability density function f(z | θ) = (g2 + θ) 2,0-1(1-2), 0<x<1.0>0 obtain a method of moments estimator for θ, θ. Calculate an estimate using this estimator when x! = 0.50. r2 = 0.75, хз = 0.85, x4= 0.25.
Will thumbs up if done neatly and correctly! 6-7. Let θ > 1 and let X1,X2, ,Xn be a random sample from the distri- bution with probability density function f(x; θ-zind, 1 < x < θ. 6. a) Obtain the maximum likelihood estimator of θ, θ b) Is a consistent estimator of θ? Justify your answer 6-7. Let θ > 1 and let X1,X2, ,Xn be a random sample from the distri- bution with probability density function f(x; θ-zind, 1
Suppose that x1, . . . , xn are a random sample having probability density function f(x; θ) = (θ + 1)x^θ , 0 < x < 1. (1) Here the parameter θ > 0. (a) Show that P(Xi ≤ b; θ) = b^(θ+1) for f(x; θ) given in (1). (b) Suppose now that because of a recurring computer glitch in storing the observations, only a random subset of the xi are observed. For the rest of the observations, it...
20. Let Xi, X2, function Xn be a random sample from a population X with density C")pr(1-0)rn-r for x = 0, 1.2, , m f(x:0) = 0 otherwise, , where 0 〈 θく1 is parameter. Show that unbiased estimator of θ for a fixed m. is a uniform minimum variance 20. Let Xi, X2, function Xn be a random sample from a population X with density C")pr(1-0)rn-r for x = 0, 1.2, , m f(x:0) = 0 otherwise, , where...