. Let X1, . . . , Xn be a random sample from a population with mean µ and variance σ2 .
Verify Vn =
→ σ2in a.s.
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,
X3, and X4 be a random
sample of observations from a population with mean μ and
variance σ2. Consider the following estimator of
μ: 1 = 0.15 X1 +
0.35 X2 + 0.20 X3 + 0.30
X4. Using the linear combination of random
variables rule and the fact that X1, ...,
X4are independently drawn from the population, calculate
the variance of 1?
A.
0.55 σ2
B.
0.275 σ2
C.
0.125 σ2
D.
0.20 σ2
Let X1, X2, ..., Xn be a random sample of size 5 from a normal population with mean 0 and variance 1. Let X6 be another independent observation from the same population. What is the distribution of these random variables? i) 3X5 – X6+1 ii) W, = - X? iii) Uz = _1(X; - X5)2 iv) Wą +xz v) U. + x vi) V5Xe vii) 2X
Let X1, . . . , Xn be a random sample from a population X with p.d.f fθ(x) = θ xθ−1 , for 0 < x < 1 0, otherwise, where θ > 1 is parameter. Find the MLE of 1/θ. If it is an unbiased estimator of 1/θ, compare its variance with the Cramer-Rao lower bound.
3. [6 pts] Let X1, . . . , Xn be a random sample frorn a distribution with variance σ2 < oo. Find cov(X, -X,x) for i = 1, ,n.
3. [6 pts] Let X1, . . . , Xn be a random sample frorn a distribution with variance σ2
Let X1 Xn be a random sample of size n from a Bernoulli population with parameter p. Show that p= X is the UMVUE for p. 5.4.22
Let X1 Xn be a random sample of size n from a Bernoulli population with parameter p. Show that p= X is the UMVUE for p. 5.4.22
1. Let Xi, X2,.., Xn be a random sample drawn from some population with mean μ--2λ and variance σ2-4, where λ is a parameter. Define 2n We use V, to estimate λ. (a) Show that is an unbiased estimator for λ. (b) Let ơin be the variance of V,, . Show that lin ơi,-
1. Let Xi, X2,.., Xn be a random sample drawn from some population with mean μ--2λ and variance σ2-4, where λ is a parameter. Define 2n...
Let X = (X1, . . . , Xn) be a random sample of size n with mean μ and variance σ2. Consider Tm i=1 (a) Find the bias of μη(X) for μ. Also find the bias of S2 and ỡXX) for σ2. (b) Show that Hm(X) is consistent. (c) Suppose EIXI < oo. Show that S2 and ỡXX) are consistent.
Let X = (X1, . . . , Xn) be a random sample of size n with mean μ...
Let X1, . . . , Xn be a random sample from a population with
density
8. Let Xi,... ,Xn be a random sample from a population with density 17 J 2.rg2 , if 0<、〈릉 0 , if otherwise ( a) Find the maximum likelihood estimator (MLE) of θ . (b) Find a sufficient statistic for θ (c) Is the above MLE a minimal sufficient statistic? Explain fully.
Let X1, . . . , Xn be a random sample from a normal distribution, Xi ∼ N(µ, σ^2 ). Find the UMVUE of σ ^2 .