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Consider a population with mean ๐ and variance ๐ 2 < โ . You are comparing two estimators ๐ ฬ 1 and ๐ ฬ 2 for the mean of the population ๐ , with the following expected values and variances ๐ธ ( ๐ ฬ 1 ) = ๐ ; ๐ ( ๐ ฬ 1 ) = 9 ; ๐ธ ( ๐ ฬ 1 ) = ๐ + 1 ; ๐ ( ๐ ฬ 2 ) = 1 . We also know that the covariance between the two estimators is ๐ถ ๐ ๐ ( ๐ ฬ 1 , ๐ ฬ 2 ) = โ 2 . Now consider a new estimator that combines the two previous ones ๐ ฬ 3 = 1 4 ๐ ฬ 1 + 3 4 ๐ ฬ 2 . Then the variance ๐ ( ๐ ฬ 3 ) of ๐ ฬ 3 is
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A.3
B.๐
2
-2
C.1.125
D.๐
2
+ 1.125
E.2.25
F.0.375
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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 explanation็ปๅฝๅณๅฏๆฅ็ๅฎๆด็ญๆก
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Consider a population with mean ฮผ and variance ฯ2<โ. 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)=โ1. 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 ๐ฟ ( ๐ฅ 1 , . . . , ๐ฅ ๐ ) = โ ๐ก = 1 ๐ ๐ ๐ฅ ๐ก ( 1 โ ๐ ) 1 โ ๐ฅ ๐ก . If you estimate the parameter ๐ using a Maximum Likelihood estimator, you obtain the point estimate ๐ ฬ = 1 ๐ โ ๐ก = 1 ๐ ๐ฅ ๐ก , which corresponds to the sample mean. We know that for a Bernoulli random variable the expected value and the variance are ๐ผ ( ๐ฅ ๐ก ) = ๐ , ๐ ( ๐ฅ ๐ก ) = ๐ ( 1 โ ๐ ) . Using this information, what is the variance of the estimator ๐ ( ๐ ฬ ) ?
ไฝ็ฝฎ2็้ฎ้ข 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.TrueFalse้ข็ฎ่งฃๆ
Consider a population with mean ฮผ and variance ฯ2<โ. 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)=โ1. 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
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