![[4 marks] Find the approximate distribution of Y = 2X1-X2, when the sample size n is large.](http://img.homeworklib.com/images/023c78cf-d308-4385-a62c-ddd6d7cc723e.png?x-oss-process=image/resize,w_560)
[4 marks] Find the approximate distribution of Y = 2X1-X2, when the sample size n is large.
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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...
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)...
3. [20 marks] Consider the multinomial distribution with 3 categories, where the random variables Xi, X2 and X3 have th...
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)...
3. [20 marks Consider the multinomial distribution with 3 categories, where the random variables Xi, X2 and Xs have the...
3. [20 marks Consider the multinomial distribution with 3 categories, where the random variables Xi, X2 and Xs have the joint probability function (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) 4 marks Find the approximate distribution of Y 2X-X2, when the sample size n is large (e) [4 marks] Assume that X-(253, 234, 513). Find the...
The joint probability mass function of random variables X and Y is given by if x1...
The joint probability mass function of random variables X and Y is given by if x1 = 1,2; x2 = 1,2 p(x1, x2) = { otherwise (a) Specify the probability mass function of X1 and X2. (b) Are X1 and X2 independent? Are they identically distributed? Explain. (C) Find the probability of the event that X1 + 2X2 > 3. (d) Find the probability of the event that X1 X2 > 2.
two random variables x1 and x2 have a joint probability density function f(x1,x2)={x1+x2, 0<x1<1, 0<x2<1 0,...
two random variables x1 and x2 have a joint probability density function f(x1,x2)={x1+x2, 0<x1<1, 0<x2<1 0, otherwise what is the marginal distribution of x1 and x2
The discrete random variables ? and ? have joint probability function ?, where ? is given...
The discrete random variables ? and ? have joint probability function ?, where ? is given by the following table: X 1 2 3 4 1 0.1 0.2 0.1 0.05 Y 2 0.05 0 0.1 0.1 3 0 0.2 0.05 0.05 a) Determine ?(1 < ? ≤ 3, 1 ≤ ? ≤ 2). [4 marks] b) Calculate ?(?^2 ?). [4 marks] c) Find the marginal probability functions ? and ℎ of ? and ? respectively. [4 marks] d) Are ?...
Let Ņ, X1. X2, . . . random variables over a probability space It is assumed that N takes nonnegative inteqer values. Let Zmax [X1, -. .XN! and W-min\X1,... ,XN Find the distribution function of Z...
Let Ņ, X1. X2, . . . random variables over a probability space It is assumed that N takes nonnegative inteqer values. Let Zmax [X1, -. .XN! and W-min\X1,... ,XN Find the distribution function of Z and W, if it suppose N, X1, X2, are independent random variables and X,, have the same distribution function, F, and a) N-1 is a geometric random variable with parameter p (P(N-k), (k 1,2,.)) b) V - 1 is a Poisson random variable with...
2. Random variables X and Y have joint probability density function f(x, y) = kry, 0<<1,0...
2. Random variables X and Y have joint probability density function f(x, y) = kry, 0<<1,0 <y <1. Assume that n independent pairs of observations (C,y:) have been made from this density function. (a) Find the k which makes f(x,y) a valid density function, (b) Find the maximum likelihood estimators of a and B. (c) Find approximate variances for â and B.
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi , . . . , X,.), V=min(X1, ,X,). (a) Find the distribution functio...
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi , . . . , X,.), V=min(X1, ,X,). (a) Find the distribution function and the density function of U and of V (b) Show that the joint density function of U and V is fe,y(u, u)= n(n-1)/(u)/(v)[F(v)-F(u)]n-1, ifu < u.
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi...
Question 4: (5 Marks) Let X and Y be continuous random variables have a joint probability...
Question 4: (5 Marks) Let X and Y be continuous random variables have a joint probability density function of the form: f(x,y) = cy2 + x 0 SX S1, 0 Sys1. Determine the following: 1. The value of c. 2. The marginal distributions f(x) and f(y). 3. The conditional distribution f(xly). 4. Are X and Y independent? Why? - the
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)...
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)...
3. [20 marks Consider the multinomial distribution with 3 categories, where the random variables Xi, X2 and Xs have the joint probability function (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) 4 marks Find the approximate distribution of Y 2X-X2, when the sample size n is large (e) [4 marks] Assume that X-(253, 234, 513). Find the...
The joint probability mass function of random variables X and Y is given by if x1 = 1,2; x2 = 1,2 p(x1, x2) = { otherwise (a) Specify the probability mass function of X1 and X2. (b) Are X1 and X2 independent? Are they identically distributed? Explain. (C) Find the probability of the event that X1 + 2X2 > 3. (d) Find the probability of the event that X1 X2 > 2.
2. Random variables X and Y have joint probability density function f(x, y) = kry, 0<<1,0 <y <1. Assume that n independent pairs of observations (C,y:) have been made from this density function. (a) Find the k which makes f(x,y) a valid density function, (b) Find the maximum likelihood estimators of a and B. (c) Find approximate variances for â and B.
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi , . . . , X,.), V=min(X1, ,X,). (a) Find the distribution function and the density function of U and of V (b) Show that the joint density function of U and V is fe,y(u, u)= n(n-1)/(u)/(v)[F(v)-F(u)]n-1, ifu < u.
(7) Let X1,Xn are i.i.d. random variables, each with probability distribution F and prob- ability density function f. Define U=max{Xi...
Question 4: (5 Marks) Let X and Y be continuous random variables have a joint probability density function of the form: f(x,y) = cy2 + x 0 SX S1, 0 Sys1. Determine the following: 1. The value of c. 2. The marginal distributions f(x) and f(y). 3. The conditional distribution f(xly). 4. Are X and Y independent? Why? - the
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