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The Pareto probability distribution has many applications in economics, biology, and physics. Let β> 0 and...
2- 5. The Weibull distribution has many applications in reliability engineering, survival analysi...
2- 5. The Weibull distribution has many applications in reliability engineering, survival analysis, and general insurance. Let B>0, 8>0. Consider the probability density function x>0 zero otherwise Recall (Homework #1) V-Χδ has an Exponential(8-T )-Gamma(u-l,e-1 ) distribution. Let X1, . , X/ be a random sample from the above probability distribution. y-ΣΧ.Σν i has a Gamma(u-n, θ- 1 ) distribution. !!! i-l 2. suppose δ is known. Let Xi, X2, , Xn be a random sample from the distribution with...
Exercise: Let Yİ,Y2, ,, be a random sample from a Gamma distribution with parameters and β. Assum...
Exercise: Let Yİ,Y2, ,, be a random sample from a Gamma distribution with parameters and β. Assume α > 0 is known. a. Find the Maximum Likelihood Estimator for β. b. Show that the MLE is consistent for β. c. Find a sufficient statistic for β. d. Find a minimum variance unbiased estimator of β. e. Find a uniformly most powerful test for HO : β-2 vs. HA : β > 2. (Assume P(Type!Error)- 0.05, n 10 and a -...
1. Suppose that xi,..., xn are a random sample having probability density function f(x; δ)-¡0 otherwise...
1. Suppose that xi,..., xn are a random sample having probability density function f(x; δ)-¡0 otherwise (a) Determine the method of moments estimator of δ based on the first moment. (b) Determine the MLE of δ.
QUESTION 2 Let Xi.. Xn be a random sample from a N (μ, σ 2) distribution,...
QUESTION 2 Let Xi.. Xn be a random sample from a N (μ, σ 2) distribution, and let S2 and Š-n--S2 be two estimators of σ2. Given: E (S2) σ 2 and V (S2) - ya-X)2 n-l -σ (a) Determine: E S2): (l) V (S2); and (il) MSE (S) (b) Which of s2 and S2 has a larger mean square error? (c) Suppose thatnis an estimator of e based on a random sample of size n. Another equivalent definition of...
be a random sample from the density 16 1. Let Xi, . f(x; β) otherwise 8(1-/4)....
be a random sample from the density 16 1. Let Xi, . f(x; β) otherwise 8(1-/4). You may suppose that E(X)(/ (a) Find a sufficient statistic Y for B and Var(X) C21 C2] 031 (b) Find the maximum likelihood estimator B of B and show that it is a function (c) Determine the Rao-Cramér lower bound (RCLB) for the variance of unbiased (d) Use the following data and maximum likelihood estimator to give an approxi- 2.66, 2.02, 2.02, 0.76, 1.70,...
1. The Weibull distribution has many applications in reliability engineering, survival analysis, and general insurance. Let...
1. The Weibull distribution has many applications in reliability engineering, survival analysis, and general insurance. Let β > 0, δ > 0. Consider the probability density function x>0 zero otherwise. Find the probability distribution of W-X a Determine the probability distribution of W by finding the c.d.f. of W, F w(w). i Find the c.d.f. of X, Fx(x) "Hint" 1: u-substitution: u- "Hint" 2: "Hint" 3: Should be FX(0)-0, P(Xs) There is no such thing as a negative cumulative distribution...
Let X be a random variable with probability density function (pdf) given by fx(r0)o elsewhere where θ 0 is an unknown parameter. (a) Find the cumulative distribution function (cdf) for the random var...
Let X be a random variable with probability density function (pdf) given by fx(r0)o elsewhere where θ 0 is an unknown parameter. (a) Find the cumulative distribution function (cdf) for the random variable Y = θ and identify the distribution. Let X1,X2, . . . , Xn be a random sample of size n 〉 2 from fx (x10). (b) Find the maximum likelihood estimator, Ỗmle, for θ (c.) Find the Uniform Minimum Variance Unbiased Estimator (UMVUE), Bumvue, for 0...
Suppose Xi, X2, ,Xn is an iid N(μ, c2μ2 sample, where c2 is known. Let μ and μ denote the method ...
Suppose Xi, X2, ,Xn is an iid N(μ, c2μ2 sample, where c2 is known. Let μ and μ denote the method of moments and maximum likelihood estimators of μ, respectively. (a) Show that ~ X and μ where ma = n-1 Σηι X? is the second sample (uncentered) moment. (b) Prove that both estimators μ and μ are consistent estimators. (c) Show that v n(μ-μ)-> N(0, σ ) and yM(^-μ)-+ N(0, σ ). Calculate σ and σ . Which estimator...
1. Let X1, ..., Xn be a random sample from a distribution with the pdf le-x/0,...
1. Let X1, ..., Xn be a random sample from a distribution with the pdf le-x/0, x > 0, N = (0,00). (a) Find the maximum likelihood estimator of 0. (b) Find the method of moments estimator of 0. (c) Are the estimators in a) and b) unbiased? (d) What is the variance of the estimators in a) and b)? (e) Suppose the observed sample is 2.26, 0.31, 3.75, 6.92, 9.10, 7.57, 4.79, 1.41, 2.49, 0.59. Find the maximum likelihood...
Let > 0 and let X1, X2, ..., Xn be a random sample from the distribution with the probability density function f(x;...
Let > 0 and let X1, X2, ..., Xn be a random sample from the distribution with the probability density function f(x; 1) = 212x3e-dız?, x > 0. a. Find E(X), where k > -4. Enter a formula below. Use * for multiplication, / for divison, ^ for power, lam for \, Gamma for the function, and pi for the mathematical constant 11. For example, lam^k*Gamma(k/2)/pi means ik r(k/2)/ I. Hint 1: Consider u = 1x2 or u = x2....
2- 5. The Weibull distribution has many applications in reliability engineering, survival analysis, and general insurance. Let B>0, 8>0. Consider the probability density function x>0 zero otherwise Recall (Homework #1) V-Χδ has an Exponential(8-T )-Gamma(u-l,e-1 ) distribution. Let X1, . , X/ be a random sample from the above probability distribution. y-ΣΧ.Σν i has a Gamma(u-n, θ- 1 ) distribution. !!! i-l 2. suppose δ is known. Let Xi, X2, , Xn be a random sample from the distribution with...
Exercise: Let Yİ,Y2, ,, be a random sample from a Gamma distribution with parameters and β. Assume α > 0 is known. a. Find the Maximum Likelihood Estimator for β. b. Show that the MLE is consistent for β. c. Find a sufficient statistic for β. d. Find a minimum variance unbiased estimator of β. e. Find a uniformly most powerful test for HO : β-2 vs. HA : β > 2. (Assume P(Type!Error)- 0.05, n 10 and a -...
1. Suppose that xi,..., xn are a random sample having probability density function f(x; δ)-¡0 otherwise (a) Determine the method of moments estimator of δ based on the first moment. (b) Determine the MLE of δ.
QUESTION 2 Let Xi.. Xn be a random sample from a N (μ, σ 2) distribution, and let S2 and Š-n--S2 be two estimators of σ2. Given: E (S2) σ 2 and V (S2) - ya-X)2 n-l -σ (a) Determine: E S2): (l) V (S2); and (il) MSE (S) (b) Which of s2 and S2 has a larger mean square error? (c) Suppose thatnis an estimator of e based on a random sample of size n. Another equivalent definition of...
be a random sample from the density 16 1. Let Xi, . f(x; β) otherwise 8(1-/4). You may suppose that E(X)(/ (a) Find a sufficient statistic Y for B and Var(X) C21 C2] 031 (b) Find the maximum likelihood estimator B of B and show that it is a function (c) Determine the Rao-Cramér lower bound (RCLB) for the variance of unbiased (d) Use the following data and maximum likelihood estimator to give an approxi- 2.66, 2.02, 2.02, 0.76, 1.70,...
1. The Weibull distribution has many applications in reliability engineering, survival analysis, and general insurance. Let β > 0, δ > 0. Consider the probability density function x>0 zero otherwise. Find the probability distribution of W-X a Determine the probability distribution of W by finding the c.d.f. of W, F w(w). i Find the c.d.f. of X, Fx(x) "Hint" 1: u-substitution: u- "Hint" 2: "Hint" 3: Should be FX(0)-0, P(Xs) There is no such thing as a negative cumulative distribution...
Let X be a random variable with probability density function (pdf) given by fx(r0)o elsewhere where θ 0 is an unknown parameter. (a) Find the cumulative distribution function (cdf) for the random variable Y = θ and identify the distribution. Let X1,X2, . . . , Xn be a random sample of size n 〉 2 from fx (x10). (b) Find the maximum likelihood estimator, Ỗmle, for θ (c.) Find the Uniform Minimum Variance Unbiased Estimator (UMVUE), Bumvue, for 0...
Suppose Xi, X2, ,Xn is an iid N(μ, c2μ2 sample, where c2 is known. Let μ and μ denote the method of moments and maximum likelihood estimators of μ, respectively. (a) Show that ~ X and μ where ma = n-1 Σηι X? is the second sample (uncentered) moment. (b) Prove that both estimators μ and μ are consistent estimators. (c) Show that v n(μ-μ)-> N(0, σ ) and yM(^-μ)-+ N(0, σ ). Calculate σ and σ . Which estimator...
1. Let X1, ..., Xn be a random sample from a distribution with the pdf le-x/0, x > 0, N = (0,00). (a) Find the maximum likelihood estimator of 0. (b) Find the method of moments estimator of 0. (c) Are the estimators in a) and b) unbiased? (d) What is the variance of the estimators in a) and b)? (e) Suppose the observed sample is 2.26, 0.31, 3.75, 6.92, 9.10, 7.57, 4.79, 1.41, 2.49, 0.59. Find the maximum likelihood...
Let > 0 and let X1, X2, ..., Xn be a random sample from the distribution with the probability density function f(x; 1) = 212x3e-dız?, x > 0. a. Find E(X), where k > -4. Enter a formula below. Use * for multiplication, / for divison, ^ for power, lam for \, Gamma for the function, and pi for the mathematical constant 11. For example, lam^k*Gamma(k/2)/pi means ik r(k/2)/ I. Hint 1: Consider u = 1x2 or u = x2....
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