Let X1, . . . , Xn be iid normal random variables with µ unknown and σ2 = 1. We know that Xbar is an UMVUE for µ. Find an UMVUE for µ3
Let X1, . . . , Xn be iid normal random variables with µ unknown and...
Let X1, . . . , Xn ∼ iid log Normal (µ, σ^2 ) for σ^ 2 known. Find the LRT for H0 : µ = µ_0 vs H1 : µ not= µ_0. f(x)=(2π)^(-1/2)(xσ)^(-1)*exp(-(ln x-µ)^2 /(2σ^2))
Let X1, . . . , Xn be a random sample from a normal distribution, Xi ∼ N(µ, σ^2 ). Find the UMVUE of σ ^2 .
Let X1, ..., Xn be a random sample (i.i.d.) from a normal distribution with parameters µ, σ2 . (a) Find the maximum likelihood estimation of µ and σ 2 . (b) Compare your mle of µ and σ 2 with sample mean and sample variance. Are they the same?
Let X1,X2,...,Xn be iid exponential random variables with unknown mean β. (b) Find the maximum likelihood estimator of β. (c) Determine whether the maximum likelihood estimator is unbiased for β. (d) Find the mean squared error of the maximum likelihood estimator of β. (e) Find the Cramer-Rao lower bound for the variances of unbiased estimators of β. (f) What is the UMVUE (uniformly minimum variance unbiased estimator) of β? What is your reason? (g) Determine the asymptotic distribution of the...
Let X1, . . . , Xn be a random sample from a normal distribution, Xi ∼ N(µ, σ^2 ). Find the UMVUE of σ ^2 .
Let X1, X2 be iid, normal(µ, σ2 = 1). Show that the statistic T = X1 + X2 is sufficient for µ
Let X1, X2, . . . , Xn be IID N(0, σ2 ) variables. Find the rejection region for the likelihood ratio test at level α = 0.1 for testing H0 : σ2 = 1 vs H1 : σ2 = 2.
3. Let X1, . . . , Xn be iid random variables with mean μ and variance σ2. Let X denote the sample mean and V-Σ,(X,-X)2 a) Derive the expected values of X and V b) Further suppose that Xi,...,Xn are normally distributed. Let Anxn - ((a) be an orthogonal matrix whose first row is (mVm Y = (y, . . . ,%), and X = (Xi, , Xn), are (column) vectors. (It is not necessary to know aij for...
1. Let X1, X2, , Xn be independent Normal μ, σ2) random variables. Let y,-n Σ_lx, denote a sequence of random variables (a) Find E(y,) and Var(y,) for all n in terms of μ and σ2. (b) Find the PDF for Yn for alln. (c) Find the MGF for Yn for all n.
Let X1,…, Xn be a sample of iid Bin(1, ?) random variables, and let T = X(1 − X) be an estimator of Var(Xi ) = ?(1 − ?). Determine E(T). Bias(T; ?(1 − ?)).