Let X1, X2, . . . be iid random variables following a uniform
distribution on the...
Let X1, X2, . . . be iid random variables following a uniform
distribution on the interval [0, θ]. Show that max(X1, . . . , Xn)
→ θ in probability as n → ∞
Solutions
Expert Solution
TOPIC:Convergence in probability.
Hence , by the sufficient condition of probability convergence,
we can say that, X(n) =max.{
X1,X2,....,Xn} converges to
, in probability.(PROVED).
Let X1, X2, . . . , Xn be iid following a uniform distribution
over the interval (θ, 2θ) (θ > 0).
(a) Find a method of moments estimator of θ.
(b) Find the MLE of θ.
(c) Find a constant k such that E(k ˆθ) = θ.
(d) By using the Rao-Blackwell, which estimators of (a) and (b)
can be improved?
Suppose X1, X2, . . . are a sequence of iid uniform random
variables with probability density function f(x) = 1 for 0 < x
< 1, or 0, otherwise.
Let Y be a continuous random variable with probability density
function g(y) = 3y2 for 0 < y < 1, or 0,
otherwise.
We further assume that Y and X1, X2, . . . are independent. Let
T = min{n ≥ 1 : Xn < Y }. That is, T...
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, . . . , Xn be a random sample from the uniform
distribution with pdf f(x; θ1, θ2) =
1/(2θ2), θ1 − θ2 < x <
θ1 + θ2, where −∞ < θ1 < ∞
and θ2 > 0, and the pdf is equal to zero
elsewhere.
(a) Show that Y1 = min(Xi) and Yn = max(Xi), the joint
sufficient statistics for θ1 and θ2, are
complete.
(b) Find the MVUEs of θ1 and θ2.
Problem 1 Let X1, X2, . . . , Xn be independent Uniform(0,1)
random variables. (
a) Compute the cdf of Y := min(X1, . . . , Xn).
(b) Use (a) to compute the pdf of Y .
(c) Find E(Y ).
Let X1,X2,...,Xn be a random sample from a uniform distribution
on the interval (0,a). Recall that the maximum likelihood estimator
(MLE) of a is ˆ a = max(Xi).
a) Let Y = max(Xi). Use the fact that Y ≤ y if and only if each Xi
≤ y to derive the cumulative distribution function of Y.
b) Find the probability density function of Y from cdf.
c) Use the obtained pdf to show that MLE for a (ˆ a =...
Let
X1,X2,...Xn
be a random sample of size n form a uniform distribution
on the interval [θ1,θ2].
Let Y = min
(X1,X2,...,Xn).
(a) Find the density function for Y. (Hint: find the
cdf and then differentiate.)
(b) Compute the expectation of Y.
(c) Suppose θ1= 0. Use part (b) to give an
unbiased estimator for θ2.
Consider random variables X1, X2, and X3 with binomial
distribution, uniform, and normal probability density functions
(PDF) respectively. Generate list of 50 random values, between 0
and 50, for these variables and store them with names Data1, Data2,
and Data3 respectively.
( To complete successfully this homework on Stochastic Models,
you need to use one of the software tools: Excel, SPSS or
Mathematica, to answer the following items, and print out your
results directly from the software.)
Let X1 and X2 be two independent random
variables having a chi-squared distribution with degrees of freedom
n1 and n2, respectively. Let
Y1 = (X1) / (X1 + X2)
and Y2 = X1 + X2
(a) Find the joint p.d.f. of Y1 and Y2
(b) Find the marginal p.d.f. of each of Y1 and
Y2
(c) Are Y1 and Y2 independent ?
Justify your answer.