Question

In: Statistics and Probability

Let X1,X2,...,Xn be i.i.d. Gamma random variables with parameters α and λ. The likelihood function is...

Let X1,X2,...,Xn be i.i.d. Gamma random variables with parameters α and λ. The likelihood function is difficult to differentiate because of the gamma function. Rather than finding the maximum likelihood estimators, what are the method of moments estimators of both parameters α and λ?

Solutions

Expert Solution


Related Solutions

1) Suppose X1,X2,...,Xn are iid Gamma(α,λ) random variables. a) Express the first and third moments (µ1...
1) Suppose X1,X2,...,Xn are iid Gamma(α,λ) random variables. a) Express the first and third moments (µ1 = E(X) and µ3 = E(X3)) as functions of λ and α. b) Solve this system of equations to find estimators of ˆ λ and ˆ α as functions of the empirical moments. Note that the estimates must be positive. 2) Suppose that X1,X2,...,Xn are a iid Beta(a, a) random variables. That is, a beta distribution with the restriction that b = a. Using...
Suppose X1, X2, . . ., Xn are iid Poisson random variables with parameter λ. (a)...
Suppose X1, X2, . . ., Xn are iid Poisson random variables with parameter λ. (a) Find the MVUE for λ. (b) Find the MVUE for λ 2
R simulation: Let X1, . . . , Xn be i.i.d. random variables from a uniform...
R simulation: Let X1, . . . , Xn be i.i.d. random variables from a uniform distribution on [0, 2]. Generate and plot 10 paths of sample means from n = 1 to n = 40 in one figure for each case. Give some comments to empirically check the Law of Large Numbers. (a) When n is large, X1 + · · · + Xn/n  converges to E[Xi]. (b) When n is large, X1^2+ · · · + Xn^2/n converges to...
6.42 Let X1,..., Xn be an i.i.d. sequence of Uniform (0,1) random variables. Let M =...
6.42 Let X1,..., Xn be an i.i.d. sequence of Uniform (0,1) random variables. Let M = max(X1,...,Xn). (a) Find the density function of M. (b) Find E[M] and V[M].
Let X1, ..., Xn be i.i.d random variables with the density function f(x|θ) = e^(θ−x) ,...
Let X1, ..., Xn be i.i.d random variables with the density function f(x|θ) = e^(θ−x) , θ ≤ x. a. Find the Method of Moment estimate of θ b. The MLE of θ (Hint: Think carefully before taking derivative, do we have to take derivative?)
1. Homework 4 Due: 2/28 Let X1,...,Xn i.i.d. Gamma(α,β) with α > 0, β > 0...
1. Homework 4 Due: 2/28 Let X1,...,Xn i.i.d. Gamma(α,β) with α > 0, β > 0 (a) Assume both α and β are unknown, find their momthod of moment estimators: αˆMOM and βˆMOM. (b) Assume α is known and β is unknown, find the maximum likelihood estimation for β. 2.
2. Let X1, X2, . . . , Xn be independent, uniformly distributed random variables on...
2. Let X1, X2, . . . , Xn be independent, uniformly distributed random variables on the interval [0, θ]. (a) Find the pdf of X(j) , the j th order statistic. (b) Use the result from (a) to find E(X(j)). (c) Use the result from (b) to find E(X(j)−X(j−1)), the mean difference between two successive order statistics. (d) Suppose that n = 10, and X1, . . . , X10 represents the waiting times that the n = 10...
Let X1, X2, . . . , Xn be iid Poisson random variables with unknown mean...
Let X1, X2, . . . , Xn be iid Poisson random variables with unknown mean µ 1. Find the maximum likelihood estimator of µ 2.Determine whether the maximum likelihood estimator is unbiased for µ
Let X1, X2, X3, . . . be independently random variables such that Xn ∼ Bin(n,...
Let X1, X2, X3, . . . be independently random variables such that Xn ∼ Bin(n, 0.5) for n ≥ 1. Let N ∼ Geo(0.5) and assume it is independent of X1, X2, . . .. Further define T = XN . (a) Find E(T) and argue that T is short proper. (b) Find the pgf of T. (c) Use the pgf of T in (b) to find P(T = n) for n ≥ 0. (d) Use the pgf of...
Let X1,X2,X3 be i.i.d. N(0,1) random variables. Suppose Y1 = X1 + X2 + X3, Y2...
Let X1,X2,X3 be i.i.d. N(0,1) random variables. Suppose Y1 = X1 + X2 + X3, Y2 = X1 −X2, Y3 =X1 −X3. Find the joint pdf of Y = (Y1,Y2,Y3)′ using : Multivariate normal distribution properties.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT