Additional Question i.i.d. ˆ Fix θ > 0 and let X1,...,Xn ∼
Unif[0,θ]. We saw in class that the MLE of θ, θMLE = max(X1, . . .
, Xn), is biased. I give two other estimators of θ, which can be
made unbiased by appropriate choice of constants C1, C2:![ADDITIONAL QUESTION Fix θ 0 and let Xi, . . . , Xn iid. Unifl0.0]. We saw in class that the MLE of θ, θΜ1E- max(Xi,..., Xn), is biased. I give two other estimators of 6, which can be made unbiased by appropriate choice of constants C1, C2: and We have two questions: (1) Find values of C1, C2 for which these estimators are unbiased. Note that C1, C2 may depend on n. 2) Which of these estimators is better? Provide some justification. Note that this question has many possible answers, as there are many ways that one estimator can be better than another. You should supply a reasonable criterion, then figure out which one is better according to this criterion](http://img.homeworklib.com/questions/80eb2440-7553-11ea-8642-97b0e61c05b9.png?x-oss-process=image/resize,w_560)
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Additional Question i.i.d. ˆ Fix θ > 0 and let X1,...,Xn ∼
Unif[0,θ]. We saw in...
Additional Question Fix θ > 0 and let X1, . . . , Xn i.i.d. ∼...
Additional Question Fix θ > 0 and let X1, . . . , Xn i.i.d. ∼ Unif[0, θ]. We saw in class that the MLE of θ, ˆθMLE = max(X1, . . . , Xn), is biased. I give two other estimators of θ, which can be made unbiased by appropriate choice of constants C1, C2: ˆθ1 = C1 max(X1, . . . , Xn) and ˆθ2 = C2Σxi We have two questions: (1) Find values of C1, C2 for...
Fix θ > 0 and let X1, . . . , Xn tid. Unif0.θ]. We saw...
Fix θ > 0 and let X1, . . . , Xn tid. Unif0.θ]. We saw in class that the MLE of θ, θΜLE- max(Xi,... , Xn), is biased. I give two other estimators of θ, which can be made unbiased by appropriate choice of constants C1, C2 1C max(Xi,... ,Xn) and We have two questions (1) Find values of C1, C2 for which these estimators are unbiased. Note that C1, C2 may depend on n (2) Which of these...
Fix θ > 0 and let Xi, , x, i d. Unif[0.0]. We saw in class...
Fix θ > 0 and let Xi, , x, i d. Unif[0.0]. We saw in class that the MLE of θ, oMLE- I give two other estimators of θ, which can be made unbiased by appropriate choice of -C1 max(Xs , . . . , X,) max(X., Xn), is biased. constants C1,C2 We have two questions: (1) Find values of C1, C2 for which these estimators are unbiased. Note that Ci,C2 may depend on n (2) Which of these estimators...
Let X1. . . . Xn be i.i.d Uniform over the interval (θ, θ + 1].Show...
Let X1. . . . Xn be i.i.d Uniform over the interval (θ, θ + 1].Show that X(1)+X(n) )/2- 1/2 is also an unbiased estimator of θ, whereX(1) is the minimum order statistic and X(n) is the maximum order statistic. If X - 1/2 is also an unbiased estimator of θ which of the two estimators would you prefer to use.
7. Let X1, · · · , Xn be i.i.d. with the density p(x, θ) = θ k (1 − θ) 1−k I{x = 0, 1} (a) Find the ML estimator of θ. (...
7. Let X1, · · · , Xn be i.i.d. with the density p(x, θ) = θ k
(1 − θ) 1−k I{x = 0, 1}
(a) Find the ML estimator of θ.
(b) Is it unbiased ?
(c) Compute its MSE
7. Let Xi, . . . , Xn be i.id, with the density p(z,0)-gk(1-0)1-k1(z-0, 1) (b) Is it unbiased? (c) Compute its MSE
7. Let Xi, . . . , Xn be i.id, with the density p(z,0)-gk(1-0)1-k1(z-0, 1)...
1. Suppose that X Unif(0, 30) and we draw a random sample X1,..., Xn Find the...
1. Suppose that X Unif(0, 30) and we draw a random sample X1,..., Xn Find the MME and compute its relative efficiency to 6, = 2X1-3X2. 2. In class, I showed the below picture. Here, I have changed the vertical axis from variance to SD. In this new picture, how can we visualize the MSE? How does this way of seeing the MSE help us decide which of two (possibly biased) estimators is more efficient? SD 04 Bias (B) 0...
Let X1,... Xn i.i.d. random variable with the following riemann density: with the unknown parameter θ...
Let X1,... Xn i.i.d. random variable with the following riemann density: with the unknown parameter θ E Θ : (0.00) (a) Calculate the distribution function Fo of Xi (b) Let x1, .., xn be a realization of X1, Xn. What is the log-likelihood- function for the parameter θ? (c) Calculate the maximum-likelihood-estimator θ(x1, , xn) for the unknown parameter θ
Let X1, ..., Xn be IID observations from Uniform(0, θ). T(X) = max(X1, . . ....
Let X1, ..., Xn be IID observations from Uniform(0, θ). T(X) = max(X1, . . . Xn) is a sufficient statistic (additionally, T is the MLE for θ). Find a (1 − α)-level confidence interval for θ. [Note: The support of this distribution changes depending on the value of θ, so we cannot use Fisher’s approximation for the MLE because not all of the regularity assumptions hold.]
4. Let Xi,..., Xn be a random sample with density 303 for 0 < θ < x NOTE: We have previously foun...
4. Let Xi,..., Xn be a random sample with density 303 for 0 < θ < x NOTE: We have previously found that θMLE-X(1) and that FX(1) (x)-1-(!)3m (a) Using the probability integral transform method, find a pivot for 0 based on the MLE. (b) Use the pivot found in (a) to get an ezact 100(1-a)% C.1. for θ (c) Find an approximate 100(1-a)% C.1. for θ based on our result for the MLE. (d) Suppose that we get n...
Exercise 8.41. The random variables X1,..., Xn are i.i.d. We also know that ElXl] = 0. EĮKY = a and Elx?| = b. Let Xn-Xi+n+Xn. Find the third moment of Xn Exercise 8.41. The random variables X1,...
Exercise 8.41. The random variables X1,..., Xn are i.i.d. We also know that ElXl] = 0. EĮKY = a and Elx?| = b. Let Xn-Xi+n+Xn. Find the third moment of Xn
Exercise 8.41. The random variables X1,..., Xn are i.i.d. We also know that ElXl] = 0. EĮKY = a and Elx?| = b. Let Xn-Xi+n+Xn. Find the third moment of Xn
Fix θ > 0 and let X1, . . . , Xn tid. Unif0.θ]. We saw in class that the MLE of θ, θΜLE- max(Xi,... , Xn), is biased. I give two other estimators of θ, which can be made unbiased by appropriate choice of constants C1, C2 1C max(Xi,... ,Xn) and We have two questions (1) Find values of C1, C2 for which these estimators are unbiased. Note that C1, C2 may depend on n (2) Which of these...
Fix θ > 0 and let Xi, , x, i d. Unif[0.0]. We saw in class that the MLE of θ, oMLE- I give two other estimators of θ, which can be made unbiased by appropriate choice of -C1 max(Xs , . . . , X,) max(X., Xn), is biased. constants C1,C2 We have two questions: (1) Find values of C1, C2 for which these estimators are unbiased. Note that Ci,C2 may depend on n (2) Which of these estimators...
7. Let X1, · · · , Xn be i.i.d. with the density p(x, θ) = θ k
(1 − θ) 1−k I{x = 0, 1}
(a) Find the ML estimator of θ.
(b) Is it unbiased ?
(c) Compute its MSE
7. Let Xi, . . . , Xn be i.id, with the density p(z,0)-gk(1-0)1-k1(z-0, 1) (b) Is it unbiased? (c) Compute its MSE
7. Let Xi, . . . , Xn be i.id, with the density p(z,0)-gk(1-0)1-k1(z-0, 1)...
1. Suppose that X Unif(0, 30) and we draw a random sample X1,..., Xn Find the MME and compute its relative efficiency to 6, = 2X1-3X2. 2. In class, I showed the below picture. Here, I have changed the vertical axis from variance to SD. In this new picture, how can we visualize the MSE? How does this way of seeing the MSE help us decide which of two (possibly biased) estimators is more efficient? SD 04 Bias (B) 0...
Let X1,... Xn i.i.d. random variable with the following riemann density: with the unknown parameter θ E Θ : (0.00) (a) Calculate the distribution function Fo of Xi (b) Let x1, .., xn be a realization of X1, Xn. What is the log-likelihood- function for the parameter θ? (c) Calculate the maximum-likelihood-estimator θ(x1, , xn) for the unknown parameter θ
4. Let Xi,..., Xn be a random sample with density 303 for 0 < θ < x NOTE: We have previously found that θMLE-X(1) and that FX(1) (x)-1-(!)3m (a) Using the probability integral transform method, find a pivot for 0 based on the MLE. (b) Use the pivot found in (a) to get an ezact 100(1-a)% C.1. for θ (c) Find an approximate 100(1-a)% C.1. for θ based on our result for the MLE. (d) Suppose that we get n...
Exercise 8.41. The random variables X1,..., Xn are i.i.d. We also know that ElXl] = 0. EĮKY = a and Elx?| = b. Let Xn-Xi+n+Xn. Find the third moment of Xn
Exercise 8.41. The random variables X1,..., Xn are i.i.d. We also know that ElXl] = 0. EĮKY = a and Elx?| = b. Let Xn-Xi+n+Xn. Find the third moment of Xn
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