![1. Let Y1, . . . ,Y,, be a random sample from a population with density function 0, otherwise (a) Find the method of moments estimator of θ (b) Show that Yan.-max(Yi, . . . ,%) is sufficient for 02] (Hint: Recall the indicator function given by I(A)1 if A is true and 0 otherwise.) (c) Determine the density function of Yn) and hence find a function of Ym) that is an unbiased estimator of θ (d) Find c so that and hence determine a 100(1-α)% confidence interval for θ (e) Suppose n 10 and to test Ho : θ 1 against H1 : θ 1.05 the null hypothesis is rejected if Y(n)2 0.99 i. What is the probability of committing a Type I error? ii. What is the probability of committing a Type II error? 13) 13]](http://img.homeworklib.com/questions/f54a04b0-78ca-11ea-a0b1-1f85ec87f9b6.png?x-oss-process=image/resize,w_560)
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1. Let Y1, . . . ,Y,, be a random sample from a population with density...
2. Let X1,.n be a random sample from the density 0 otherwise Suppose n = 2m+...
2. Let X1,.n be a random sample from the density 0 otherwise Suppose n = 2m+ 1 for some integer m. Let Y be the sample median and Z = max(Xi) be the sample maximum (a) Apply the usual formula for the density of an order statistic to show the density of Y is (b) Note that a beta random variable X has density f(x) = TaT(可 with mean μ = α/(a + β) and variance σ2 = αβ/((a +s+...
2. Let Yı, ..., Yn be a random sample from an Exponential distribution with density function...
2. Let Yı, ..., Yn be a random sample from an Exponential distribution with density function e-, y > 0. Let Y(1) minimum(Yi, , Yn). (a) Find the CDE of Y) b) Find the PDF of Y (c) Is θ-Yu) is an unbiased estimator of θ? Show your work. (d) what modification can be made to θ so it's unbiased? Explain.
. Suppose the Y1, Y2, · · · , Yn denote a random sample from a...
. Suppose the Y1, Y2, · · · , Yn denote a random sample from a
population with Rayleigh distribution (Weibull distribution with
parameters 2, θ) with density function f(y|θ) = 2y θ e −y 2/θ, θ
> 0, y > 0
Consider the estimators ˆθ1 = Y(1) = min{Y1, Y2, · · · , Yn},
and ˆθ2 = 1 n Xn i=1 Y 2 i .
ii) (10 points) Determine if ˆθ1 and ˆθ2 are unbiased
estimators, and in...
4. Let Yi, ½, . . . , Yn be a random sample from some pdf/pmf...
4. Let Yi, ½, . . . , Yn be a random sample from some pdf/pmf f(y; θ)·Let W be a point estimator h(y, Y2, . . . , Yn) for θ. The bias of W as a point estimator for θ is defined as W Blase(W) = E(W)- The mean square error of W is defined as MSEe(W) = E(W-0)2 (a) Using properties of expected values, and the definition of variance from PSTAT 120A/B, show that MSEe(W) = Vare(W)...
Let Y1,...,Yn be a sample from the density f(y) = 3y2/θ3,0 ≤ y ≤ θ. Calculate...
Let Y1,...,Yn be a sample from the density f(y) = 3y2/θ3,0 ≤ y ≤ θ. Calculate the mean of this distribution and find the Method of Moments estimate of θ.
Problem 2.1. Let Y1, ...,Yn be a random sample from a uniform distribution on the interval...
Problem 2.1. Let Y1, ...,Yn be a random sample from a uniform distribution on the interval [0 – 1,20 + 1]. a. Find the density function of X = Y;-0 (note that Yi ~ Uf0 - 1,20 + 1]). b. Find the density function of Y(n) = max{Y;, i = 1,...,} c. Find a moment estimator of . d. Use the following data to obtain a moment estimate for 4: 11.72 12.81 12.09 13.47 12.37.
(1 point) Let Yı, Y2, ..., Yn be a random sample from the probability density function...
(1 point) Let Yı, Y2, ..., Yn be a random sample from the probability density function f(yla) = |aya-2/5° f(y ) 0 <y< 5 otherwise 0 for > -1. Find an estimator for a using the method of moments.
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 -...
10. Let Y1,..., Y, be a random sample from a distribution with pdf 0<y< elsewhere f(x)...
10. Let Y1,..., Y, be a random sample from a distribution with pdf 0<y< elsewhere f(x) = { $(0 –» a) Find E(Y). b) Find the method of moments estimator for 8. c) Let X be an estimator of 8. Is it an unbiased estimator? Find the mean square error of X. Show work
Let Y1, Y2, ..., Yn be independent random variables each having uniform distribution on the interval...
Let Y1, Y2, ..., Yn be independent random variables
each having uniform distribution on the interval (0, θ).
(a) Find the distribution of Y(n) and find its expected
value.
(b) Find the joint density function of Y(i) and Y(j) where 1 ≤ i
< j ≤ n. Hence
find Cov(Y(i)
, Y(j)).
(c) Find var(Y(j) − Y(i)).
Let Yİ, Ya, , Yn be independent random variables each having uniform distribu- tion on the interval (0, 6) (a) Find the distribution...
2. Let X1,.n be a random sample from the density 0 otherwise Suppose n = 2m+ 1 for some integer m. Let Y be the sample median and Z = max(Xi) be the sample maximum (a) Apply the usual formula for the density of an order statistic to show the density of Y is (b) Note that a beta random variable X has density f(x) = TaT(可 with mean μ = α/(a + β) and variance σ2 = αβ/((a +s+...
2. Let Yı, ..., Yn be a random sample from an Exponential distribution with density function e-, y > 0. Let Y(1) minimum(Yi, , Yn). (a) Find the CDE of Y) b) Find the PDF of Y (c) Is θ-Yu) is an unbiased estimator of θ? Show your work. (d) what modification can be made to θ so it's unbiased? Explain.
. Suppose the Y1, Y2, · · · , Yn denote a random sample from a
population with Rayleigh distribution (Weibull distribution with
parameters 2, θ) with density function f(y|θ) = 2y θ e −y 2/θ, θ
> 0, y > 0
Consider the estimators ˆθ1 = Y(1) = min{Y1, Y2, · · · , Yn},
and ˆθ2 = 1 n Xn i=1 Y 2 i .
ii) (10 points) Determine if ˆθ1 and ˆθ2 are unbiased
estimators, and in...
4. Let Yi, ½, . . . , Yn be a random sample from some pdf/pmf f(y; θ)·Let W be a point estimator h(y, Y2, . . . , Yn) for θ. The bias of W as a point estimator for θ is defined as W Blase(W) = E(W)- The mean square error of W is defined as MSEe(W) = E(W-0)2 (a) Using properties of expected values, and the definition of variance from PSTAT 120A/B, show that MSEe(W) = Vare(W)...
Problem 2.1. Let Y1, ...,Yn be a random sample from a uniform distribution on the interval [0 – 1,20 + 1]. a. Find the density function of X = Y;-0 (note that Yi ~ Uf0 - 1,20 + 1]). b. Find the density function of Y(n) = max{Y;, i = 1,...,} c. Find a moment estimator of . d. Use the following data to obtain a moment estimate for 4: 11.72 12.81 12.09 13.47 12.37.
(1 point) Let Yı, Y2, ..., Yn be a random sample from the probability density function f(yla) = |aya-2/5° f(y ) 0 <y< 5 otherwise 0 for > -1. Find an estimator for a using the method of moments.
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 -...
10. Let Y1,..., Y, be a random sample from a distribution with pdf 0<y< elsewhere f(x) = { $(0 –» a) Find E(Y). b) Find the method of moments estimator for 8. c) Let X be an estimator of 8. Is it an unbiased estimator? Find the mean square error of X. Show work
Let Y1, Y2, ..., Yn be independent random variables
each having uniform distribution on the interval (0, θ).
(a) Find the distribution of Y(n) and find its expected
value.
(b) Find the joint density function of Y(i) and Y(j) where 1 ≤ i
< j ≤ n. Hence
find Cov(Y(i)
, Y(j)).
(c) Find var(Y(j) − Y(i)).
Let Yİ, Ya, , Yn be independent random variables each having uniform distribu- tion on the interval (0, 6) (a) Find the distribution...
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