Let the dynamics Xi , i = 1, 2, . . . , d be independently and identically distributed as Z ∼ N(0, 1). One approach for modeling the short-term interest rate rt at any time t is given by defining rt ∆= X2 1 + X2 2 + . . . + X2 d .
(a) Describe the distribution of the continuous random variable rt.
(b) Find the probability that rt ∈ (0, 0.07] if d = 3.
(c) Find the probability that rt ∈ (0, 0.07] if d = 5.


2. Let Xi exp(1) and X2 ~ variables with rate 1. Let: erp(1) be independent and identically-distributed exponential random (a) What is the cdf of X1? b) What is the joint pdf of (Xi, X2)? (c) What is the joint pdf of (Y, Z)? d) What is the marginal pdf of z?
PLEASE SOLVE ONLY QUESTION B
B. Let
be identically and independently distributed exponential random
variables with each having probability density function
. Then, find the probability density function of
HINT- Use the following decomposition:
A. LetX1,X2, ..., Xn be identically and independently distributed random variables with each having zero mean and variance σ. If j is defined as z,-X -X, j -1,2,..n where 7t k-1 then find E(Z,) and Var Z)
2. [12 marksj Let Xi and X2 be independent and identically distributed random variables, each having an exponential distribution with density function (x),foro, 0, elsewbere Pdof W Let W = X1 +X2 and's Use the -method-of transformatiou- to find jhe joint probability density fuactíion of-W andy. AreWandfindependent?AThy? M covered m w, r 201 Instead tyto ind pdf of w b methed of colf
Exercise 7. Let Xi, X2, . . . be independent, identically distributed rundorn variables uithEX and Var(X) 9, and let Yǐ = Xi/2. We also define Tn and An to be the sum and the sample mean, respectively, of the random variablesy, ,Y,- 1) Evaluate the mean and variance of Yn, T,, and A (2) Does Yn converge in probability? If so, to what value? 3) Does Tn converge in probability? If so, to what value? (4) Does An converge...
2. Let Xi, X2, . Xn be a random sample from a distribution with the probability density function f(x; θ-829-1, 0 < x < 1,0 < θ < oo. Find the MLE θ
Let Xi,X2, , Xn be independent and identically distributed (ii.d.) Exponential(1) random variables. 14] [41 (a) Find the method of moments estimator for X (b) Find the method of moments estimator for (c) Find the bias, variance and MSE (mean square erop) for the essimator in part () Total: [16]
Let Xi,X2, , Xn be independent and identically distributed (ii.d.) Exponential(1) random variables. 14] [41 (a) Find the method of moments estimator for X (b) Find the method of moments...
5. (8 marks) Let X1, X2, ..., X6 be identically and independently distributed standard nor- mal random variables. Let X = Lit. What is the distribution of the random variable (a) W = L X}; (b) U = L-1(X; – ); (e) 230; and (d) 265x4+x3) ? You need to provide the answer as well as justification.
D. Let Xi, X2,. be independent random variables from a uniform distribution over the interval [0, 1]. Prove that the sequence X+XX. converges in probability and find the limit
1. The random variables Xi, X2,.. are independent and identically distributed (iid), each with pdf f given in Assignment 4, Question 1. Let Sn- Xi+.+X Using the Central Limit Theorem and the graph of the standard normal distribution in Figure 1, approximate the probability P(S100 >600). Express your answer in the format x.x-10-x. Verify your answer by simulating 10,000 outcomes of Si00 and counting how many of them are > 600. Show the code 1.00 0.95 0.90 0.85 1.2 1.4...
Let ?1, ... , ?10 are identically independently distributed (iid) with Gamma(2, ?) a) Compute the likelihood function (LF). b) Adopt the appropriate conjugate prior to the parameter ? (Hint: Choose hyperparameters optionally within the support of distribution). c) Using (a) and (b), find the posterior distribution of ?. d) Compute the minimum Bayesian risk estimator of ?.