9. Suppose that X1, X2, ..., Xn are ind a). Show that the pdf of X(n)...
Suppose X1 and X2 are continuous random variables with join pdf given by f(x1, x2) = 2(x1 + x2) if 0 < x1 < x2 < 1, and zero otherwise. (a) Find P(X2 > 2X1). (b) Find the marginal pdf of X2. (c) Find the conditional pdf of X1 given X2 = x2.
Question 1: Suppose that X1, X2,... Xn are independent identically distributed continuous outcome random variables which have a probability density function (pdf) f(z) = π1+ア Calculate (with all working) the pdf of the average of the X,i Comment on the significance of this result to sampling from a random vari- able with the pdf f. This pdf is called a Cauchy density.
Let λ >0 and suppose that X1,X2,...,Xn be i.i.d. random variables with Xi∼Exp(λ). Find the PDF of X1+···+Xn. Use convolution formula and prove by induction
Let X1, X2, · · · be independent random variables, Xn ∼ U(−1/n, 1/n). Let X be a random variable with P(X = 0) = 1. (a) what is the CDF of Xn? (b) Does Xn converge to X in distribution? in probability?
Suppose that X1,X2,. X are iid random variables with pdf ,220 (a) Find the maximum likelihood estimate of the parameter a (b) Find the Fisher Information of X1,X2,.. ., Xn and use it to estimate a 95% confidence interval on the MLE of a (c) Explain how the central limit theorem relates to (b).
X1, X2, ..., Xn constitute a random sample from a population with pdf 2 +0.03) |2|<1 f(0) = 0 {ila. 0.W. where 101 < 1. Determine if X is an unbiased estimator of 8. If not, modify it to make it unbiased, and determine if it is consistent. Justify.
Let X0, X1, X2,... be a
branching process (as defined in class), i.e. Xn gives
then number of individuals in the nth generation. Suppose that the
mean number of offspring per individual is μ. Show that
Mn = μ-nXn is a martingale with
respect to X0, X1, X2,...
Let Xo, X1, X2,... be a branching process (as defined in class), i.e., Xn gives then number of individuals in the nth generation. Suppose that the mean number of offspring per individual...
5. Let X1, X2, ..., Xn be a random sample from a distribution with pdf of f(x) = (@+1)xº,0<x<1. a. What is the moment estimator for 0 using the method of moments technique? b. What is the MLE for @ ?
5. Let X1, X2,. , Xn be a random sample from a distribution with pdf of f(x) (0+1)x,0< x<1 a. What is the moment estimator for 0 using the method of moments technique? b. What is the MLE for 0?
Suppose the random variable X has probability density function (pdf) - { -1 < x<1 otherwise C fx (x) C0 : where c is a constant. (a) Show that c = 1/7; (b) Graph fx (х); (c) Given that all of the moments exist, why are all the odd moments of X zero? (d) What is the median of the distribution of X? (e) Find E (X2) and hence var X; (f) Let X1, fx (x) What is the limiting...