Show that for every n ∈ N and every sequence X1, . . . , Xn of independent random variables with distribution P, the random variable n min(X1 , . . . , Xn ) also has distribution P.
Let Ņ, X1. X2, . . . random variables over a probability space It is assumed that N takes nonnegative inteqer values. Let Zmax [X1, -. .XN! and W-min\X1,... ,XN Find the distribution function of Z and W, if it suppose N, X1, X2, are independent random variables and X,, have the same distribution function, F, and a) N-1 is a geometric random variable with parameter p (P(N-k), (k 1,2,.)) b) V - 1 is a Poisson random variable with...
Consider a sequence of random variables X1, ..., Xn, ..., where for each n, Xn~ tn. We will use Slutsky's Theorem to show that as the degrees of freedom go to infinity, the distribution converges to a standard normal. (a) Let V1, ..., Vn, ... be such that Vn ~ X2. Find the value b such that Vn/n þy b. (b) Letting U~ N(0,1), show that In = ☺ ~tn and that Tn "> N(0,1). VVn/n
Consider a sequence of random variables X1, . . . , Xn, . . .where for each n, Xn ∼ t distribution. Apply Slutsky’s Theorem to show that as the degrees of freedom go to infinity, the distribution converges to a standard normal. (a) Let V1, . . . , V_n, . . . be such that Vn ∼ Chi Sq, n df. Find the value b such that V/n in probability −→ b. (b) Letting U ∼ N(0, 1),...
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?
Problem 5: 10 points Consider n independent variables, {X1, X2,... , Xn) uniformly distributed over the unit interval, (0,1) Introduce two new random variables, M-max (X1, X2,..., Xn) and N -min (X1, X2,..., Xn) 1. Find the joint distribution of a pair (M,N) 2. Derive the CDF and density for M 3. Derive the CDF and density for N.
Let X1, X2,...be a sequence of random variables. Suppose that Xn?a in probability for some a ? R. Show that (Xn) is Cauchy convergent in probability, that is, show that for all > 0 we have P(|Xn?Xm|> )?0 as n,m??.Is the converse true? (Prove if “yes”, find a counterexample if “no”)
Escalate - Show every mynute steps in DETAILED! Explain & SHOW how the LIMITS of integration & DOMAIN are found. Let X1, X2, ... be independent Uniform(0,1)-distributed random variables, and let N be a Poisson(1) random variable independent of X1, X2, .... Let X(n) = max{X1, X2, ..., Xn} for n > 1. Determine the distribution of X(N+1). Hint: First derive the conditional pdf or cdf of X (N+1) given that N = n. Then use the law of total...
(Stochastic process and probability theory) Let Xn, n > 1, denote a sequence of independent random variables with E(Xn) = p. Consider the sequence of random variables În = n(n-1) {x,x, which is an unbiased estimator of up. Does (a) in f H² ? (6) ûn 4* H?? (c) în + k in mean square? (d) Does the estimator în follow a normal distribution if n + ?
74. Let X1, X2, ... be a sequence of independent identically distributed contin- uous random variables. We say that a record occurs at time n if X > max(X1,..., Xn-1). That is, X, is a record if it is larger than each of X1, ... , Xn-1. Show (i) P{a record occurs at time n}=1/n; (ii) E[number of records by time n] = {}_1/i; (iii) Var(number of records by time n) = 2/_ (i - 1)/;2; (iv) Let N =...
Question 6 Let X1, . . . , Xn denote a sequence of independent and identically distributed i.id. N(14x, σ2) random variables, and let Yı, . . . , Yrn denote an independent sequence of iid. Nụy, σ2) ran- dom variables. il Λί and Y is an unbiased estimator of μ for any value of λ in the unit interval, i.e. 0 < λ < 1. 2. Verify that the variance of this estimator is minimised when and determine the...