Let X be a R.V. with a gamma distribution and the following parameters (X~(α, 1)). What is the pdf and the cdf of Y = X/β, where β > 0 . What is the name of this type of distribution?
The pdf function of X is
Now,
The cumulative density function of Y = X/β is
and the probability density function of Y can be written as
So,
Let X be a R.V. with a gamma distribution and the following parameters (X~(α, 1)). What...
3.13 Let X,..., X be i.i.d. r.v.'s from the Gamma distribution with parameters a known and β θ eQ (0,0) unknown. (i) Determine the Fisher information I(e). U = U (X, , ,X" ) = ' (ii) Show that the estimate ηα 1.1 is unbiased and calculate its variance.
Two questions exist : ) if it has pdf. A railon variable X has the l'areio distril illi ribution with parameters m, a (m, α > 0 w 0 otherwise Show that if X has this Pareto distribution, then the random variable log(X/m) has the expo- nential distribution with parameter α Let X ~ Gamma(α, β), where α > 1 . Find E[1/X]. ) if it has pdf. A railon variable X has the l'areio distril illi ribution with parameters...
3. Suppose that X has the gamma distribution with parameters α and β. (a) Determine the mode of X. (Be careful about the range of a) (b) Let c be a positive constant. Show that cX has the gamma distribution with parar neters and ß/c.
Suppose that X has a gamma distribution with parameters α > 0 and β>0. Show that if a is any value so that α+a>0 then E[X^a] = (β^aΓ(α + a))/Γ(a)
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 -...
Let X ~ Gamma(k, β) and Y ~ Gamma(k, 1) Gamma( α, 3) Cx Show that Y = 스 is a pivot Let X ~ Gamma(k, β) and Y ~ Gamma(k, 1) Gamma( α, 3) Cx Show that Y = 스 is a pivot
U means Uniform distribution 2. Let X be a r.v. distributed as U(α, β). Show that its ch. f. and m.g.f.x and Mr, respectively, are given by and IM x it(β-a) , t(B-a) ii) By differentiating (ax, show that E(X)-(α + β) / 2 and T 2 (X)-(α-β)2 / 12.
Y1, Y2, ... Yn are a random sample from the Gamma distribution with parameters α and β (a) Suppose that α-4 is known and β is unknown. Find a complete sufficient statistic for β. Find the MVUE of β. (Hint: What is E(Y)?) (b) Suppose that β = 4 is known and a is unknown. Find a complete sufficient statistic for α.
with parameters α and β. 2. Yİ,%, , Y, are a random sample from the Gamma distribution (a) Suppose that α 4 is known and β is unknown. Find a complete sufficient statistic for β. Find the MVUE of β. (Hint: What is E(Y)?) (b) Suppose that β 4 is known and α is unknown. Find a complete sufficient statistic for a. with parameters α and β. 2. Yİ,%, , Y, are a random sample from the Gamma distribution (a)...
Let X1 ,……, Xn be a random sample from a Gamma(α,β) distribution, α> 0; β> 0. Show that T = (∑n i=1 Xi, ∏ n i=1 Xi) is complete and sufficient for (α, β).