5. Let X ∼ Exp(λ) with λ unknown, and suppose X1, X2 is a random sample of size 2. Show that M = sqrt( X1 · X2 ) is a biased estimator of 1/λ and modify it to create an unbiased estimator. (Hint: During your journey, you’ll need the help of the gamma distribution, the gamma function, and the knowledge that Γ(1/2) = √ π.)
5. Let X ∼ Exp(λ) with λ unknown, and suppose X1, X2 is a random sample...
5. Let X ~ Exp(A) with λ unknown, and suppose X1,X2 is a random sample of size 2, Show that M-X (Hint: During your journey, you' need the help of the gamma distribution, the gamma function, and the knowledge that Г(1/2-ут) X1 X2 is a biased estimator of - and modify it to create an unbiased estimator
0 and an Let X1, X2, ..., Xn be a random sample where each X; follows a normal distribution with mean u unknown standard deviation o. Let K (n-1)s2 = n 202 (a) [2 points] Assume K ~ Gamma(a = n71,8 bias for K. *). We wish to use K as an estimator of o2. Compute the n (b) [1 point] If K is a biased estimator for o?, state the function of K that would make it an unbiased...
Let X1,X2 be two independent exponential random variables with λ=1, compute the P(X1+X2<t) using the joint density function. And let Z be gamma random variable with parameters (2,1). Compute the probability that P(Z < t). And what you can find by comparing P(X1+X2<t) and P(Z < t)? And compare P(X1+X2+X3<t) Xi iid (independent and identically distributed) ~Exp(1) and P(Z < t) Z~Gamma(3,1) (You don’t have to compute) (Hint: You can use the fact that Γ(2)=1, Γ(3)=2) Problem 2[10 points] Let...
Suppose X1, X2, . . . , Xn (n ≥ 5) are i.i.d. Exp(µ) with the density f(x) = 1 µ e −x/µ for x > 0. (a) Let ˆµ1 = X. Show ˆµ1 is a minimum variance unbiased estimator. (b) Let ˆµ2 = (X1 +X2)/2. Show ˆµ2 is unbiased. Calculate V ar(ˆµ2). Confirm V ar(ˆµ1) < V ar(ˆµ2). Calculate the efficiency of ˆµ2 relative to ˆµ1. (c) Show X is consistent and sufficient. (d) Show ˆµ2 is not consistent...
Let X1, X2, ..., Xn be a random sample from a Gamma( a , ) distribution. That is, f(x;a,0) = loga xa-le-210, 0 < x <co, a>0,0 > 0. Suppose a is known. a. Obtain a method of moments estimator of 0, 0. b. Obtain the maximum likelihood estimator of 0, 0. c. Is O an unbiased estimator for 0 ? Justify your answer. "Hint": E(X) = p. d. Find Var(ë). "Hint": Var(X) = o/n. e. Find MSE(Ô).
Let X1, X2, X3, and X4 be a random sample of observations from a population with mean μ and variance σ2. The observations are independent because they were randomly drawn. Consider the following two point estimators of the population mean μ: 1 = 0.10 X1 + 0.40 X2 + 0.40 X3 + 0.10 X4 and 2 = 0.20 X1 + 0.30 X2 + 0.30 X3 + 0.20 X4 Which of the following statements is true? HINT: Use the definition of...
Let λ >0 and suppose that X1,X2,...,Xn be i.i.d. random variables with Xi∼Exp(λ). Find the PDF of X1+···+Xn. Use convolution formula and prove by induction
Let X1, X2, ...... Xn be a random sample of size n from EXP() distribution , , zero , elsewhere. Given, mean of distribution and variances and mgf a) Show that the mle for is . Is a consistent estimator for ? b)Show that Fisher information . Is mle of an efficiency estimator for ? why or why not? Justify your answer. c) what is the mle estimator of ? Is the mle of a consistent estimator for ? d) Is...
Let X1, X2,... X,n be a random sample of size n from a distribution with probability density function obtain the maximum likelihood estimator of λ, λ. Calculate an estimate using this maximum likelihood estimator when 1 0.10, r2 0.20, 0.30, x 0.70.
Problem 3: Suppose X1, X2, is a sequence of i.i.d. random variables having the Poisson distribution with mean λ. Let A,-X, (a) Is λη an unbiased estimator of λ? Explain your answer. (b) Is in a consistent estimator of A? Explain your answer 72