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To show that an estimator can be consistent without being unbiased or even asymptotically the finite...
To show an estimator can be consistent without being unbiased or even asymptotically unbiased, consider the...
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...
To show that an estimator can be consistent without being unbiased or even asymptotically unbiased, consider...
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...
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...
10.41] To show an estimator can be consistent without being unbiased or even asymptotically unbiased, consider...
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...
Find a consistent estimator of µ 2 , where E(Y ) = µ is the population mean and Y¯ n is the sample mean. 2 If E(Y 2 ) =...
Find a consistent estimator of µ 2 , where E(Y ) = µ is the
population mean and Y¯ n is the sample mean. 2 If E(Y 2 ) = µ 0 2
then prove that 1 n Pn i=1 Y 2 i is an consistent estimator of µ 0
2 3 We define σ 2 = µ 0 2 − µ 2 . Show that S 2 n = 1 n Pn i=1 Y 2
i − Y¯ 2...
6. In Problem 1, show that θ2 is a consistent estimator for θ. Deduce that Y(n) is a consistent e...
Please answer as neatly as possible.
Much thanks in advance!
Question 1:
6. In Problem 1, show that θ2 is a consistent estimator for θ. Deduce that Y(n) is a consistent estimator for θ and also asyınpt○tically unbiased estimator for θ. 1. Let Yi, ½, . . . ,y, denote a random sample from an uniform distribution on the interval (0,0). We have seen that (1) and 62 Ym are unbiased estimators for 0. Find the efficiency of 6 relative...
I1. Follow the steps below to show that the pooled estimator $p is an unbi- ased...
I1. Follow the steps below to show that the pooled estimator $p is an unbi- ased estimator for the common standard deviation of two independent sam ples Let Yi, Yi2, ..., Yini denote the random sample of size n from the first population with population mean μ| and population variance σ, and let Y21, Y22, ..., Y2na denote an independent random sample of size n2 from the second population with population mean μ2 and population mean ơ3. Sup- pose that...
Show that the mean of a random sample of size n is a minimum variance unbiased estimator of the parameter of a Poisson population.
Show that the mean of a random sample of size n is a minimum variance unbiased estimator of the parameter (lambda) of a Poisson population.
5. Horvitz-Thompson (HT) estimator (a) (2 marks) Show that the HT estimator es tu/su is unbiased...
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...
CLUSTER SAMPLING WITH ESTIMATION Suppose a population of size N is divided into K- N/M groups of size M. We select a sample of size n -km the following way: » First we select k groups out of K groups...
CLUSTER SAMPLING WITH ESTIMATION Suppose a population of size N is divided into K- N/M groups of size M. We select a sample of size n -km the following way: » First we select k groups out of K groups by simple random sampling . We then select m units in each group selected on the first step by simple random sampling . The estimate of the population mean is the average Y of the sample. Let μί be the...
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...
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...
Find a consistent estimator of µ 2 , where E(Y ) = µ is the
population mean and Y¯ n is the sample mean. 2 If E(Y 2 ) = µ 0 2
then prove that 1 n Pn i=1 Y 2 i is an consistent estimator of µ 0
2 3 We define σ 2 = µ 0 2 − µ 2 . Show that S 2 n = 1 n Pn i=1 Y 2
i − Y¯ 2...
Please answer as neatly as possible.
Much thanks in advance!
Question 1:
6. In Problem 1, show that θ2 is a consistent estimator for θ. Deduce that Y(n) is a consistent estimator for θ and also asyınpt○tically unbiased estimator for θ. 1. Let Yi, ½, . . . ,y, denote a random sample from an uniform distribution on the interval (0,0). We have seen that (1) and 62 Ym are unbiased estimators for 0. Find the efficiency of 6 relative...
I1. Follow the steps below to show that the pooled estimator $p is an unbi- ased estimator for the common standard deviation of two independent sam ples Let Yi, Yi2, ..., Yini denote the random sample of size n from the first population with population mean μ| and population variance σ, and let Y21, Y22, ..., Y2na denote an independent random sample of size n2 from the second population with population mean μ2 and population mean ơ3. Sup- pose that...
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...
CLUSTER SAMPLING WITH ESTIMATION Suppose a population of size N is divided into K- N/M groups of size M. We select a sample of size n -km the following way: » First we select k groups out of K groups by simple random sampling . We then select m units in each group selected on the first step by simple random sampling . The estimate of the population mean is the average Y of the sample. Let μί be the...
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