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Suppose (X1, ..., Xk) follows a multinomial distribution with size n and event probability pi, i...
3. Suppose (X1, ..., Xk) follows a multinomial distribution with size n and event probability Pi,...
3. Suppose (X1, ..., Xk) follows a multinomial distribution with size n and event probability Pi, i = 1, ..., k. (C-1 Xi = n and Li-l pi = 1). (a) Show Xi~ Binom(pi) for i = 1, ..., k. (b) Show X; + X; ~ Binom(pi + pj), for 1 <i, j <k and i # j. (c) Show Cov(Xi, X;) = -npipj. (Hint: V(X; + X;) = V(X;) + V(X;) + 2Cov(Xi, X;)).
Question 1: Conditional of Poisson random variabless is Multinomial Lct X1,.... X% be independent random variables...
Question 1: Conditional of Poisson random variabless is Multinomial Lct X1,.... X% be independent random variables and suppose that X, ~ Poisson(Ii). What is the conditional distribution of (Xi, . . . , Xk) given that Σ_1 X,-n?
7. Suppose that Xi,..., Xk are independent random variables, and X, ~ Exp(B) for i =...
7. Suppose that Xi,..., Xk are independent random variables, and X, ~ Exp(B) for i = 1, . . . , k. Let Y = min(X1 , . . . , Xk). Show that Y ~ Exp(Σ-1 β).
3. [20 marks] Consider the multinomial distribution with 3 categories, where the random variables Xi, X2 and X3 have the joint probability function where x = (zi, 2 2:23), θ = (θί, θ2), n = x1 + 2 2...
3. [20 marks] Consider the multinomial distribution with 3 categories, where the random variables Xi, X2 and X3 have the joint probability function where x = (zi, 2 2:23), θ = (θί, θ2), n = x1 + 2 2 + x3, θι, θ2 > 0 and 1-0,-26, > 0. (a) [4 marks] Find the maximum likelihood estimator θ of θ. (b) [4 marks] Find that the Fisher information matrix I(0) (c) [4 marks] Show that θ is an MVUE. (d)...
The random vector x (XI, X2,... ,Xk)' is said to have a symmetric multivariate normal distribution...
The random vector x (XI, X2,... ,Xk)' is said to have a symmetric multivariate normal distribution if x ~ Ne(μ, Σ) where μ 1k, i.e., the mean of each X, is equal to the same constant μ, and Σ is the equicorre- lation dispersion matrix, i.e. when k 3, μ-0, σ2-2 and ρ 1/2, find the probability that Hint: Recall that if x = (Xi, , Xk), has a continuous symmetric dis tribution, then all possible permutations of X1,... ,Xk...
20 marksConsider the multinomial distribution with 3 categories, where the random variables X1,X2 and X have the joint probability function 123 [4 marks] Find the approximate distribution of Y = 2X1...
20 marksConsider the multinomial distribution with 3 categories, where the random variables X1,X2 and X have the joint probability function 123 [4 marks] Find the approximate distribution of Y = 2X1-X2, when the sample size n is large.
20 marksConsider the multinomial distribution with 3 categories, where the random variables X1,X2 and X have the joint probability function 123
[4 marks] Find the approximate distribution of Y = 2X1-X2, when the sample size n is large.
Let X1, X2, ..., Xn be a random sample of size n from the distribution with probability density function f(x;) = 2xAe-d...
Let X1, X2, ..., Xn be a random sample of size n from the distribution with probability density function f(x;) = 2xAe-de?, x > 0, 1 > 0. a. Obtain the maximum likelihood estimator of 1. Enter a formula below. Use * for multiplication, / for divison, ^ for power. Use mi for the sample mean X, m2 for the second moment and pi for the constant 1. That is, n mi =#= xi, m2 = Š X?. For example,...
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) = 212x3 e-tz, x > 0. a. Find E(XK), where k > -4. Enter a formula below. Use * for multiplication, / for divison, ^ for power, lam for 1, Gamma for the function, and pi for the mathematical constant i. For example, lam^k*Gamma(k/2)/pi means ik r(k/2)/n. Hint 1: Consider u = 1x2 or u = x2....
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y)...
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y) or each 1, and (11 CoV(X,Y) var(x)var(y) (Recall that p vararo
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J,...
suppose X1, X2 is a random sample of size n = 2 from a population distribution....
suppose X1, X2 is a random sample of size n = 2 from a
population distribution.
i) compute P(X1=X2)
ii) what is the probability that the sample mean is less than
1.5?
T 0 1 2 P(x) 0.2 0.5 0.3
3. Suppose (X1, ..., Xk) follows a multinomial distribution with size n and event probability Pi, i = 1, ..., k. (C-1 Xi = n and Li-l pi = 1). (a) Show Xi~ Binom(pi) for i = 1, ..., k. (b) Show X; + X; ~ Binom(pi + pj), for 1 <i, j <k and i # j. (c) Show Cov(Xi, X;) = -npipj. (Hint: V(X; + X;) = V(X;) + V(X;) + 2Cov(Xi, X;)).
Question 1: Conditional of Poisson random variabless is Multinomial Lct X1,.... X% be independent random variables and suppose that X, ~ Poisson(Ii). What is the conditional distribution of (Xi, . . . , Xk) given that Σ_1 X,-n?
7. Suppose that Xi,..., Xk are independent random variables, and X, ~ Exp(B) for i = 1, . . . , k. Let Y = min(X1 , . . . , Xk). Show that Y ~ Exp(Σ-1 β).
3. [20 marks] Consider the multinomial distribution with 3 categories, where the random variables Xi, X2 and X3 have the joint probability function where x = (zi, 2 2:23), θ = (θί, θ2), n = x1 + 2 2 + x3, θι, θ2 > 0 and 1-0,-26, > 0. (a) [4 marks] Find the maximum likelihood estimator θ of θ. (b) [4 marks] Find that the Fisher information matrix I(0) (c) [4 marks] Show that θ is an MVUE. (d)...
The random vector x (XI, X2,... ,Xk)' is said to have a symmetric multivariate normal distribution if x ~ Ne(μ, Σ) where μ 1k, i.e., the mean of each X, is equal to the same constant μ, and Σ is the equicorre- lation dispersion matrix, i.e. when k 3, μ-0, σ2-2 and ρ 1/2, find the probability that Hint: Recall that if x = (Xi, , Xk), has a continuous symmetric dis tribution, then all possible permutations of X1,... ,Xk...
20 marksConsider the multinomial distribution with 3 categories, where the random variables X1,X2 and X have the joint probability function 123 [4 marks] Find the approximate distribution of Y = 2X1-X2, when the sample size n is large.
20 marksConsider the multinomial distribution with 3 categories, where the random variables X1,X2 and X have the joint probability function 123
[4 marks] Find the approximate distribution of Y = 2X1-X2, when the sample size n is large.
Let X1, X2, ..., Xn be a random sample of size n from the distribution with probability density function f(x;) = 2xAe-de?, x > 0, 1 > 0. a. Obtain the maximum likelihood estimator of 1. Enter a formula below. Use * for multiplication, / for divison, ^ for power. Use mi for the sample mean X, m2 for the second moment and pi for the constant 1. That is, n mi =#= xi, m2 = Š X?. For example,...
Let > 0 and let X1, X2, ..., Xn be a random sample from the distribution with the probability density function f(x; 1) = 212x3 e-tz, x > 0. a. Find E(XK), where k > -4. Enter a formula below. Use * for multiplication, / for divison, ^ for power, lam for 1, Gamma for the function, and pi for the mathematical constant i. For example, lam^k*Gamma(k/2)/pi means ik r(k/2)/n. Hint 1: Consider u = 1x2 or u = x2....
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y) or each 1, and (11 CoV(X,Y) var(x)var(y) (Recall that p vararo
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J,...
suppose X1, X2 is a random sample of size n = 2 from a
population distribution.
i) compute P(X1=X2)
ii) what is the probability that the sample mean is less than
1.5?
T 0 1 2 P(x) 0.2 0.5 0.3
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