4. Show that SPR = Nan PTP) is an unbiased estimator of Var(Pn), where P, is...
2. Show that: When X is a binomial rv, the sample proportion is the unbiased estimator of the population proportion. IfX1.Хг, estimator of the population mean a) xn is a random sample with mean ,, then the sample mean is the unbiased b)
5. Horvitz-Thompson (HT) estimator (a) (2 marks) Show that the HT estimator es tu/su is unbiased for the population total. Clearly define any notation used. (b) (1 mark) The variance of the HT estimator is Var(ār(s)) = () Give the HT estimate of the variance based on the sample, S. (c) (1 mark) Suppose we sample with replacement where probability of selecting unit u is pu. Derive the inclusion probability for unit. (d) A sample (n = 6) was randomly...
To show that an estimator can be consistent without being unbiased or even asymptotically unbiased, consider the following estimation procedure: To estimate the mean of a population with randomly draw o slips of paper numbered from 1 through n, and if the number we draw is 2, 3,.. .or n, we use as our estimator the mean of the random sample; otherwise, we use the estimate n2. Show that this estimation procedure is (a) consistent; (b) neither unbiased nor asymptotically...
To show an estimator can be consistent without being unbiased or even asymptotically unbiased, consider the following estimation procedure: To estimate the mean of a population with the nite variance 2, we rst take a random sample of size n. Then, we randomly draw one of n slips of paper numbered from 1 through n, and if the number we draw is 2, 3, , or n, we use as our estimator the mean of the random sample; otherwise, we...
To show an estimator can be consistent without being unbiased or even asymptotically unbiased, consider the following estimation procedure: To estimate the mean of a population with the finite variance σ 2 , we first take a random sample of size n . Then, we randomly draw one of n slips of paper numbered from 1 through n , and • if the number we draw is 2, 3, ··· , or n , we use as our estimator the...
10.41] To show an estimator can be consistent without being unbiased or even asymptotically unbiased, consider the following estimation procedure: To estimate the mean of a population with the finite variance σ2, we first take a random sample of size n. Then, we randomly draw one of n slips of paper numbered from 1 through n, and if the number we draw is 2, 3, ..., orn, we use as our estimator the mean of the random sample; otherwise, we...
this is a challenging question Let X ~ POI(μ), and let θ-P(X = 0-e-". (a) Is -e-r an unbiased estimator of θ? (b) Show that θ = u(X) is an unbiased estimator of θ, where u(0) 1 and u(x)-0 if (c) Compare the MSEs of, and è for estimating θ-e-, when μ 1 and 2. Let X ~ POI(μ), and let θ-P(X = 0-e-". (a) Is -e-r an unbiased estimator of θ? (b) Show that θ = u(X) is an...
Show that median is an unbiased estimator of population mean when population is normally distributed .
Problem 5 Let Xi, X2, ..., Xn be a random sample from Bernoulli(p), 0 < p < 1, and 7.i. Prove that the sample proportion is an unbiased estimator of p, i.e. p,- is an unbiased estimator of p 7.ii. Derive an expression for the variance of p,n 7.iii. Prove that the sample proportion is a consistent estimator of p. 7.iv. Prove that pn(1- Pn)
Q1 and Q2 (please also show the steps): Q1 Prove that MSE) = Var(ë) + Bias(@?, i.e., El(Ô – 9)2) = E[(O - ECO)?] + [ECO) – 6)2. Q2 Suppose X1, X2, ..., X, are i.i.d. Bernoulli random variables with probability of success p. It is known that = is an unbiased estimator for p. n 1. Find E(2) and show that p2 is a biased estimator for p? (Hint: make use of the distribution of x. and the fact...