Question

Show that the sample variance of a random variable X is a consistent estimator of the...

Show that the sample variance of a random variable X is a consistent estimator of the population variance.

EXPLAIN

Homework Answers

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
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 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,..., or n, we use as our estimator the mean of the random sample; otherwise, we...
For a random sample of size n from a Beta(α,β) density, find a consistent estimator of...
For a random sample of size n from a Beta(α,β) density, find a consistent estimator of (α/β). Why is this estimator consistent?
We have shown that the sample mean estimator is both unbiased and consistent for the population...
We have shown that the sample mean estimator is both unbiased and consistent for the population mean. a) Give an example of an estimator for the population mean that is unbiased but not consistent b) Give an example of an estimator for population mean that is consistent but not unbiased.
a. If ? ̅1 is the mean of a random sample of size n from a...
a. If ? ̅1 is the mean of a random sample of size n from a normal population with mean ? and variance ?1 2 and ? ̅2 is the mean of a random sample of size n from a normal population with mean ? and variance ?2 2, and the two samples are independent, show that ?? ̅1 + (1 − ?)? ̅2 where 0 ≤ ? ≤ 1 is an unbiased estimator of ?. b. Find the value...
1. Show that if X is a Poisson random variable with parameter λ, then its variance...
1. Show that if X is a Poisson random variable with parameter λ, then its variance is λ 2.Show that if X is a Binomial random variable with parameters n and p, then the its variance is npq.
Answer true or false: We saw that the sufficient conditions for a consistent estimator included the...
Answer true or false: We saw that the sufficient conditions for a consistent estimator included the condition that the limit of the variance of the estimator must be zero as n increases. Since the formula for the variance of our estimated betahat2 does not include an n in our denominator, unlike the variance of the sample mean, we can prove that it is asymptotically unbiased, but we cannot claim that it is consistent.
Let X be a random variable with a mean of 9 and a variance of 16....
Let X be a random variable with a mean of 9 and a variance of 16. Let Y be a random variable with a mean of 10 and a variance of 25. Suppose the population correlation coefficient between random variables X and Y is -0.4. a) Find the mean of the random variable W = 3X - 5Y. b) Find the standard deviation of the random variable Z = X + Y
A random variable X is normally distributed. Suppose we obtain a random sample of 11 elements...
A random variable X is normally distributed. Suppose we obtain a random sample of 11 elements from the population. Further assume that the population variance is 100. Find the probability that the sample variance is at least 48.6.
1) Which of the following does not generally decrease the variance of the OLS estimator of...
1) Which of the following does not generally decrease the variance of the OLS estimator of slope βˆ1? a) Increasing the variance of the error term, b) Increasing the sample size, c) None of the above, d) Increasing the variance of the independent variable 2) If the independent variable is a binary variable then which of the following is true? a)β0 is a population mean for the group with a value of 1 for the independent variable, b) β1 is...
The random variable X is uniformly distributed in the interval [0, α] for some α >...
The random variable X is uniformly distributed in the interval [0, α] for some α > 0. Parameter α is fixed but unknown. In order to estimate α, a random sample X1, X2, . . . , Xn of independent and identically distributed random variables with the same distribution as X is collected, and the maximum value Y = max{X1, X2, ..., Xn} is considered as an estimator of α. (a) Derive the cumulative distribution function of Y . (b)...