Questions
Questions
Single choice

Consider a population with mean ๐œ‡ and variance ๐œŽ 2 < โˆž . You are comparing two estimators ๐œ‡ ฬ‚ 1 and ๐œ‡ ฬ‚ 2 for the mean of the population ๐œ‡ , with the following expected values and variances ๐ธ ( ๐œ‡ ฬ‚ 1 ) = ๐œ‡ ; ๐‘‰ ( ๐œ‡ ฬ‚ 1 ) = 9 ; ๐ธ ( ๐œ‡ ฬ‚ 1 ) = ๐œ‡ + 1 ; ๐‘‰ ( ๐œ‡ ฬ‚ 2 ) = 1 . We also know that the covariance between the two estimators is ๐ถ ๐‘‚ ๐‘‰ ( ๐œ‡ ฬ‚ 1 , ๐œ‡ ฬ‚ 2 ) = โˆ’ 2 . Now consider a new estimator that combines the two previous ones ๐œ‡ ฬ‚ 3 = 1 4 ๐œ‡ ฬ‚ 1 + 3 4 ๐œ‡ ฬ‚ 2 . Then the variance ๐‘‰ ( ๐œ‡ ฬ‚ 3 ) of ๐œ‡ ฬ‚ 3 is

Options
A.3
B.๐œŽ 2 -2
C.1.125
D.๐œŽ 2 + 1.125
E.2.25
F.0.375
View Explanation

View Explanation

Verified Answer
Please login to view
Step-by-Step Analysis
We start by identifying the given quantities for the two estimators: Var(mu1_hat) = 9, Var(mu2_hat) = 1, Cov(mu1_hat, mu2_hat) = -2, and the new estimator mu3_hat = (1/4) mu1_hat + (3/4) mu2_hat. The variance of a linear combination is Var(aX + bY) = a^2 Var(X) + b^2 Var(Y) + 2ab Cov(X, Y). Applying this: - The weight for mu1_hat is a = 1/4, so a^2 Var(mu1_hat) = (1/16) * 9 = 9/16 = 0.5625. - The wei......Login to view full explanation

Log in for full answers

We've collected overย 50,000 authentic exam questionsย andย detailed explanationsย from around the globe. Log in now and get instant access to the answers!

Similar Questions

Consider a population with mean ฮผ and variance ฯƒ2<โˆž. Assume the following two estimators ห† ฮผ 1 and ห† ฮผ 2 for the mean of the population ฮผ, with the following expected values and variances E( ห† ฮผ 1)=ฮผ;V( ห† ฮผ 1)=5; E( ห† ฮผ 1)=ฮผ+1;V( ห† ฮผ 2)=2. We also know that the covariance between the two estimators is COV( ห† ฮผ 1, ห† ฮผ 2)=โˆ’1. Now consider a new estimator that combines the two previous ones ห† ฮผ 3= 1 3 ห† ฮผ 1+ 2 3 ห† ฮผ 2. Then the variance V( ห† ฮผ 3) of ห† ฮผ 3 is

Consider the likelihood of an i.i.d. sample from a Bernoulli population with parameter ๐‘ ๐ฟ ( ๐‘ฅ 1 , . . . , ๐‘ฅ ๐‘‡ ) = โˆ ๐‘ก = 1 ๐‘‡ ๐‘ ๐‘ฅ ๐‘ก ( 1 โˆ’ ๐‘ ) 1 โˆ’ ๐‘ฅ ๐‘ก . If you estimate the parameter ๐‘ using a Maximum Likelihood estimator, you obtain the point estimate ๐‘ ฬ‚ = 1 ๐‘‡ โˆ‘ ๐‘ก = 1 ๐‘‡ ๐‘ฅ ๐‘ก , which corresponds to the sample mean. We know that for a Bernoulli random variable the expected value and the variance are ๐”ผ ( ๐‘ฅ ๐‘ก ) = ๐‘ , ๐• ( ๐‘ฅ ๐‘ก ) = ๐‘ ( 1 โˆ’ ๐‘ ) . Using this information, what is the variance of the estimator ๐• ( ๐‘ ฬ‚ ) ?

ไฝ็ฝฎ2็š„้—ฎ้ข˜ The variance of the estimator for E[Y]E\left\lbrack Y\right\rbrack at a given point x0x_0 decreases as the sample size increases.The variance of the estimator for E[Y]E\left\lbrack Y\right\rbrack at a given point x0x_0 decreases as the sample size increases.TrueFalse้ข˜็›ฎ่งฃๆž

Consider a population with mean ฮผ and variance ฯƒ2<โˆž. Assume the following two estimators ห† ฮผ 1 and ห† ฮผ 2 for the mean of the population ฮผ, with the following expected values and variances E( ห† ฮผ 1)=ฮผ;V( ห† ฮผ 1)=5; E( ห† ฮผ 1)=ฮผ+1;V( ห† ฮผ 2)=2. We also know that the covariance between the two estimators is COV( ห† ฮผ 1, ห† ฮผ 2)=โˆ’1. Now consider a new estimator that combines the two previous ones ห† ฮผ 3= 1 3 ห† ฮผ 1+ 2 3 ห† ฮผ 2. Then the variance V( ห† ฮผ 3) of ห† ฮผ 3 is

More Practical Tools for Students Powered by AI Study Helper

Join us and instantly unlock extensive past papers & exclusive solutions to get a head start on your studies!