STAT 140 Suppose that X have a gamma distribution with parameters a = 2 and θ= 3, and suppose that the conditional distribution of Y given X=x, is uniform between 0 and x.
(1) Find E(Y) and Var(Y).
(2) Find the Moment Generating Function (MGF) of Y. What is the distribution of Y?
STAT 140 Suppose that X have a gamma distribution with parameters a = 2 and θ=...
Having troubles with question 2. Please help 2. If X has a Gamma distribution with parameters a and B, then its mgf is given by (a) Obtain expressions for the moment-genérating functions of an exponential random variable and of a chi-square random variable by recognizing that these are special cases of a Gamma distribution and using the mgf given above. (b) Suppose that X1 is a Gamma variable with parameters α1 and β, X2 is a Gamma variable with parameters...
2. (2 pts) Suppose X follows a Gamma distribution with parameters a, B, and the following density function F(t) = f(a)ga Find o and 8 so that E(X) = Var(X) = 1. 3. (2 pts) Find the median for the random variable, X. in #2.
I. Let X be a random sample from an exponential distribution with unknown rate parameter θ and p.d.f (a) Find the probability of X> 2. (b) Find the moment generating function of X, its mean and variance. (c) Show that if X1 and X2 are two independent random variables with exponential distribution with rate parameter θ, then Y = X1 + 2 is a random variable with a gamma distribution and determine its parameters (you can use the moment generating...
12.5A e 2 Suppose that A has a Gamma distribution: fA(A) 「3.5)23.5 (a) Suppose that the conditional distribution of X given Λ = λ is fxA(TA z ) e- for x > 0. i. Find Ex ii. Find Var( (b) Suppose that the conditional distribution of X given A = λ is frA (zA)-Xe-k for x > 0. Find the unconditional probability density function fx(x) of *
4. The moment generating function of the normal distribution with parameters μ and σ2 is (t) exp ( μ1+ σ2t2 ) for -oo < t oo. Show that E X)-ψ(0)-μ and Var(X)-ψ"(0)-[ty(0)12-σ2. 5. Suppose that X1, X2, and X3 are independent random variables such that E[X]0 and ElX 1 for i-12,3. Find the value of E[LX? (2X1 X3)2] 6. Suppose that X and Y are random variables such that Var(X)-Var(Y)-2 and Cov(X, Y)- 1. Find the value of Var(3X -...
The moment generating function (MGF) for a certain probability distribution is given by 2 (2 + 2) , M(t) = R. t 2 Suppose Xi, X2, are iid random variables with this distribution. Let Sn -Xi+ (a) Show that Var(X) =3/2, i = 1,2. (b) Give the MGF of Sn/v3n/2. (c) Evaluate the limit of the MGF in (b) for n → 0. The moment generating function (MGF) for a certain probability distribution is given by 2 (2 + 2)...
Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-re, x > 0 ) is sufficient for θ, using x/θ the definition ofsuficiency. Then show that the mle of θ is a function of the sufficient statistic. Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-re, x > 0 ) is...
Consider the hierarchical Bayes model (a) Show that the conditional pdf g(ply, 0) is the pdf of a beta distribution with parameters (b) Show that the conditional pdf g(θ|y, p) is the pdf of a gamma distribution with parameters 2 and log p Consider the hierarchical Bayes model (a) Show that the conditional pdf g(ply, 0) is the pdf of a beta distribution with parameters (b) Show that the conditional pdf g(θ|y, p) is the pdf of a gamma distribution...
Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-",x > 0 ) is sufficient for θ, using the definition ofsuficiency. Then show that the mle of θ is a function of the sufficient x10 statistic. Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-",x > 0 ) is sufficient for...
Suppose Y is uniformly distributed on (0,1), and that the conditional distribution of X given that Y = y is uniform on (0, y). Find E[X]and Var(X).