Problem 1: Let (Xi,..., Xn) denote a random variable from X having a Log-normal density fx (x) = d(L m)/ x, x 〉 0 n(x) - where m is an unknown parameter. Show n-1 Σ'al Ln(X) is a MVU estimator f...
Let (X1, ..., Xn) denote a random variable from X having a Log-normal density fX(x) = φ(Ln(x) − m)/x, x > 0 where m is an unknown parameter. Show (1/n) sigma(i=1 to n) of Ln(Xi) is a MVU estimator for m.
Problem 2: Let (X1,... Xn) denote a random variable from X having density fx(x) = 1/ β,0 < x < β where β > 0 is an unknown param eter. Explain why the Cramer Rao Theorem cannot be applied to show that an unbiased estimator of β is MVU. (Hint: see slides. Condition (A) of Cramer Rao Theorem)
Let X be a random variable with probability density function (pdf) given by fx(r0)o elsewhere where θ 0 is an unknown parameter. (a) Find the cumulative distribution function (cdf) for the random variable Y = θ and identify the distribution. Let X1,X2, . . . , Xn be a random sample of size n 〉 2 from fx (x10). (b) Find the maximum likelihood estimator, Ỗmle, for θ (c.) Find the Uniform Minimum Variance Unbiased Estimator (UMVUE), Bumvue, for 0...
Let X1,... Xn i.i.d. random variable with the following riemann density: with the unknown parameter θ E Θ : (0.00) (a) Calculate the distribution function Fo of Xi (b) Let x1, .., xn be a realization of X1, Xn. What is the log-likelihood- function for the parameter θ? (c) Calculate the maximum-likelihood-estimator θ(x1, , xn) for the unknown parameter θ
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?
1. Suppose that xi, ,xn are a random sample having probability density function Here the parameter θ > 0. (a) Determine the log likelibood, 10), and a 1- dimensional (a) Determine the log-likelihood, l(0), and a 1-dimensional sufficient statistic. (b) Show that P(XiS b;0) = +1 for f(x:0) given in (1). (c) Suppose now that because of a recurring computer glitch in storing the observations, only a random subset of the r, are observed. For the rest of the observations,...
please answer with full soultion. with explantion. (4 points) Let Xi, , Xn denote a randon sample from a Normal N(μ, 1) distribution, with 11 as the unknown parameter. Let X denote the sample mean. (Note that the mean and the variance of a normal N(μ, σ2) distribution is μ and σ2, respectively.) Is X2 an unbiased estimator for 112? Explain your answer. (Hint: Recall the fornula E(X2) (E(X)Var(X) and apply this formula for X - be careful on the...
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...
8. Let X1,...,Xn denote a random sample of size n from an exponential distribution with density function given by, 1 -x/0 -e fx(x) MSE(1). Hint: What is the (a) Show that distribution of Y/1)? nY1 is an unbiased estimator for 0 and find (b) Show that 02 = Yn is an unbiased estimator for 0 and find MSE(O2). (c) Find the efficiency of 01 relative to 02. Which estimate is "better" (i.e. more efficient)? 8. Let X1,...,Xn denote a random...
. Suppose that x1, . . . , xn are a random sample having probability density function f(x; θ) = (θ + 1)x^θ , 0 < x < (1) Here the parameter θ > 0. (a) Determine the log-likelihood, l(θ), and a 1-dimensional sufficient statistic. (b) Show that P(Xi ≤ b; θ) = b θ+1 for f(x; θ) given in (1). (c) Suppose now that because of a recurring computer glitch in storing the observations, only a random subset of...