Suppose that Z is a continuous random variable. Let
denote
the unnormalized PDF of Z ―the function
satisfies all properties of a PDF except that it is not
normalized. Now suppose we use
to compute
something like the moment generating function (MGF), i.e., we
compute the function
What is ? How
can we use
to
normalize the PDF?
let constant = k
such that
hence
we have divide by
to normalize the pdf
Suppose that Z is a continuous random variable. Let denote the unnormalized PDF of Z ―the...
A probability density function f of a continuous random variable
x satisfies all of the following conditions except
a)
b)
c) For any a,b with
, P()
=
d) The mean and variance of a probability density function f are
both finite
We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image
Let A be a continuous random variable with probability
density function
Random variable D is given by
----------------------------------------------------------------------------------------------------------------
(a) What is the probability density function of D?
specify the domain of D.
Answer is
-
-
(b) Find E(D) and Var(D).
fa(a) = -a? 9 0<A<3 We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image
Continuous random variable X has pdf for , where is symmetric about x = 0. Evaluate where is the cumulative distribution function of X and k > 0. fr) We were unable to transcribe this imagefr) We were unable to transcribe this imageFr(r
9. Let a random variable X follow the distribution with pdf f(z)=(0 otherwise (a) Find the moment generating function for X (b) Use the moment generating function to find E(X) and Var(X)
Let X1, X2, ..., Xn be a random
sample from X which has pdf
depending on a parameter
and
(i)
(ii)
where
< x <
. In both these two cases
a) write down the log-likelihood function and find a
1-dimensional sufficient statistic for
b) find the score function and the maximum likelihood estimator
of
c) find the observed information and evaluate the Fisher
information at
= 1.
f(20) We were unable to transcribe this image((z(0 – 2) - )dxəz(47)...
Suppose X is a positive random variable with density for x > 0 and with moment generating function (a) Use the fact that for x > 0 to prove that (b) Use the result in (a) to find ExpectedValue(x-1) if X ~ Gamma(alpha, beta) where the ExpectedValue(X) = alpha(beta) fr) M, (t) We were unable to transcribe this imageExpectedValue(z-1)= 1 M1(-t)dt
Let Z be a random variable where P(X<0) = 0:
a) If
, what is
?
b) If
, what is P = [Z = E(Z)] ?
c) If
, what is
?
6,(W) = jw We were unable to transcribe this imageD() = *(1 + exp(2jw) We were unable to transcribe this imageWe were unable to transcribe this image
We denote the collecction of sets determined by the first k
tosses
Suppose the imitial stock price is ,with up and
down facter being and .
Let the probability of each and be
and
and the
sigma-lgebra generated by the coin tosses up to (and inchudling)
time
1) Compute
2) compute
3)Compute
such that
4) Compute
5)Compute
6) Compute
the smallest sigma algebra contains S3
7) Compute
n-3 n-0 u.So u.So u2.d.S So u.d2.S p u.d2s d2.So...
Suppose we have 5 independent and identically distributed random variables X1, X2, X3, X4,X5 each with the moment generating function 212 Let the random variable Y be defined as Y = Σ We were unable to transcribe this image
STATISTICS Let be a simple random sample of a given random variable with density function , , , Calculate a sufficient statistic for and an unbiased estimator for which is function of the previous sufficient statistic. Thank you for your explanations We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable...