No a,b needed. please do c and d with clear steps
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No a,b needed. please do c and d with clear steps
A mixture of m univariate...
please work out parts b,c,d with clear steps thanks A mixture of m univariate Gaussians has...
please work out parts b,c,d with clear steps thanks
A mixture of m univariate Gaussians has the PDF: X(x) - where each pi 0 and Σ-i pi-1, and N(x; μ, σ*) = (2πσ2)-1/2 exp (-(x-p?/(2σ2)) exp (-(x-μ)2 a) How many parameters does a mixture of m Gaussians have? b) Let xi, , Vn be n observations drawn from a mixture of m Gaussians. Write down the log-likelihood function. Hint: it should involve two summations c) Let 1 k < m....
Please help a) How many parameters does a mixture of m Gaussians have? b) Let x1,...
Please help
a) How many parameters does a mixture of m Gaussians
have?
b) Let x1, . . . , xn be n observations drawn from a mixture
of m Gaussians. Write down the log-likelihood function. （Hint: it
should involve two summations.）
c) Let 1 ≤ k ≤ m. Show that the maximum likelihood estimator
for µk is given b
and d)
A mixture of m univariate Gaussians has the PDF TIL where each P3 > 0 and Σ-1 pi-|...
Let Xi ~ N(μ, σ?), where ơỈ are known and positive for i-1, are independent. Let /- (a) Find the ...
Please show every step, thank you.
Let Xi ~ N(μ, σ?), where ơỈ are known and positive for i-1, are independent. Let /- (a) Find the mean and variance of μ. (b) Compare μ to X,-n-Σί.i Xi as an estimator of μ. , n, and Xi, X, , E-1(1/o .m be the MLE of μ.
Let Xi ~ N(μ, σ?), where ơỈ are known and positive for i-1, are independent. Let /- (a) Find the mean and variance of μ....
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...
FF1:18 1H20B B 80 ma2500a16-1 ma2500s14 ma2500a15 ma2500s15 ma2500a17 2. Let Xi, X2 , X10 be...
FF1:18 1H20B B 80 ma2500a16-1 ma2500s14 ma2500a15 ma2500s15 ma2500a17 2. Let Xi, X2 , X10 be a random sample of observations from the N(μ, σ*) distribution where μ is unknown and σ2-10. We reject the null hypothesis Ho : μ-5 in lavour of the alternative hypothesis H1 : μ < 5 if sum of the observations is less than or equal to 35 (a) What is the critical region for the test? (b) Compute the size of the test (c)...
5. Let Xi,..., X, be iid N(e, 1). (a) Show that X is a complete sufficient statistic. (b) Show th...
1.(c)
2.(a),(b)
5. Let Xi,..., X, be iid N(e, 1). (a) Show that X is a complete sufficient statistic. (b) Show that the UMVUE of θ 2 is X2-1/n x"-'e-x/θ , x > 0.0 > 0 6. Let Xi, ,Xn be i.i.d. gamma(α,6) where α > l is known. ( f(x) Γ(α)θα (a) Show that Σ X, is complete and sufficient for θ (b) Find ElI/X] (c) Find the UMVUE of 1/0 -e λ , X > 0 2) (x...
I don't understand a iii and b ii, What's the procedure of deriving the limit distribution? Thanks. 6. Extreme...
I don't understand a iii and b ii, What's the procedure of
deriving the limit distribution? Thanks.
6. Extreme values are of central importance in risk management and the following two questions provide the fundamental tool used in the extreme value theory. (a) Let Xi,... , Xn be independent identically distributed (i. i. d.) exp (1) random variables and define max(Xi,..., Xn) (i) Find the cumulative distribution of Zn (ii) Calculate the cumulative distribution of Vn -Zn - Inn (iii)...
1. Let X1,... , Xn be IID random points from Exp(1/B). The PDF of Exp(1/B) is...
1. Let X1,... , Xn be IID random points from Exp(1/B). The PDF of Exp(1/B) is for x 〉 0. Let X,-1 Σー X, be the sample average. Let 3 be the parameter of interest that we want to estimate. Xi be the sample average. Let B be the parameter of (a) (1 pt) What is the bias and variance of using the sample average Xn as the estimator of 3? (b) (0.5 pt) What is the mean square error...
Questions start here: Make use of the following: (a) Some probabılty densıty functions (1) 1fX ~ N (μ σ2) the probabi...
Questions start here:
Make use of the following: (a) Some probabılty densıty functions (1) 1fX ~ N (μ σ2) the probability density functlon is (n) If X~beta I (m, n) the probabılıty densıty function is (111) IfX ~ X2 (n, δ) the probability density function is (iv) If X~ Fmn(S) the probabılıty density function is 「 (m + n + 2k) x2(m+2k)- k1 (b) Some mathematıcal functions -00 (n) Γ (n)a-n-fe-arx"-'dx 0 (iv) iv) Г (n)「(m) n + m -...
Let exp(-т*) + vk Yk where dent M and V N(0, o2 are mutually indepen R, k = 1, (a) Construct the likelihood T(y|x) and...
Let exp(-т*) + vk Yk where dent M and V N(0, o2 are mutually indepen R, k = 1, (a) Construct the likelihood T(y|x) and the negative log-likelihood. (b) Compute the maximum likelihood estimate îML (c) Bonus question: How does the estimate change if E(k) t0?
Let exp(-т*) + vk Yk where dent M and V N(0, o2 are mutually indepen R, k = 1, (a) Construct the likelihood T(y|x) and the negative log-likelihood. (b) Compute the maximum likelihood estimate...
please work out parts b,c,d with clear steps thanks
A mixture of m univariate Gaussians has the PDF: X(x) - where each pi 0 and Σ-i pi-1, and N(x; μ, σ*) = (2πσ2)-1/2 exp (-(x-p?/(2σ2)) exp (-(x-μ)2 a) How many parameters does a mixture of m Gaussians have? b) Let xi, , Vn be n observations drawn from a mixture of m Gaussians. Write down the log-likelihood function. Hint: it should involve two summations c) Let 1 k < m....
Please help
a) How many parameters does a mixture of m Gaussians
have?
b) Let x1, . . . , xn be n observations drawn from a mixture
of m Gaussians. Write down the log-likelihood function. （Hint: it
should involve two summations.）
c) Let 1 ≤ k ≤ m. Show that the maximum likelihood estimator
for µk is given b
and d)
A mixture of m univariate Gaussians has the PDF TIL where each P3 > 0 and Σ-1 pi-|...
Please show every step, thank you.
Let Xi ~ N(μ, σ?), where ơỈ are known and positive for i-1, are independent. Let /- (a) Find the mean and variance of μ. (b) Compare μ to X,-n-Σί.i Xi as an estimator of μ. , n, and Xi, X, , E-1(1/o .m be the MLE of μ.
Let Xi ~ N(μ, σ?), where ơỈ are known and positive for i-1, are independent. Let /- (a) Find the mean and variance of μ....
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...
FF1:18 1H20B B 80 ma2500a16-1 ma2500s14 ma2500a15 ma2500s15 ma2500a17 2. Let Xi, X2 , X10 be a random sample of observations from the N(μ, σ*) distribution where μ is unknown and σ2-10. We reject the null hypothesis Ho : μ-5 in lavour of the alternative hypothesis H1 : μ < 5 if sum of the observations is less than or equal to 35 (a) What is the critical region for the test? (b) Compute the size of the test (c)...
1.(c)
2.(a),(b)
5. Let Xi,..., X, be iid N(e, 1). (a) Show that X is a complete sufficient statistic. (b) Show that the UMVUE of θ 2 is X2-1/n x"-'e-x/θ , x > 0.0 > 0 6. Let Xi, ,Xn be i.i.d. gamma(α,6) where α > l is known. ( f(x) Γ(α)θα (a) Show that Σ X, is complete and sufficient for θ (b) Find ElI/X] (c) Find the UMVUE of 1/0 -e λ , X > 0 2) (x...
I don't understand a iii and b ii, What's the procedure of
deriving the limit distribution? Thanks.
6. Extreme values are of central importance in risk management and the following two questions provide the fundamental tool used in the extreme value theory. (a) Let Xi,... , Xn be independent identically distributed (i. i. d.) exp (1) random variables and define max(Xi,..., Xn) (i) Find the cumulative distribution of Zn (ii) Calculate the cumulative distribution of Vn -Zn - Inn (iii)...
1. Let X1,... , Xn be IID random points from Exp(1/B). The PDF of Exp(1/B) is for x 〉 0. Let X,-1 Σー X, be the sample average. Let 3 be the parameter of interest that we want to estimate. Xi be the sample average. Let B be the parameter of (a) (1 pt) What is the bias and variance of using the sample average Xn as the estimator of 3? (b) (0.5 pt) What is the mean square error...
Questions start here:
Make use of the following: (a) Some probabılty densıty functions (1) 1fX ~ N (μ σ2) the probability density functlon is (n) If X~beta I (m, n) the probabılıty densıty function is (111) IfX ~ X2 (n, δ) the probability density function is (iv) If X~ Fmn(S) the probabılıty density function is 「 (m + n + 2k) x2(m+2k)- k1 (b) Some mathematıcal functions -00 (n) Γ (n)a-n-fe-arx"-'dx 0 (iv) iv) Г (n)「(m) n + m -...
Let exp(-т*) + vk Yk where dent M and V N(0, o2 are mutually indepen R, k = 1, (a) Construct the likelihood T(y|x) and the negative log-likelihood. (b) Compute the maximum likelihood estimate îML (c) Bonus question: How does the estimate change if E(k) t0?
Let exp(-т*) + vk Yk where dent M and V N(0, o2 are mutually indepen R, k = 1, (a) Construct the likelihood T(y|x) and the negative log-likelihood. (b) Compute the maximum likelihood estimate...
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