Let (X₁, X₂) be a pair of standard exponential random variables with the countermonotonicity copula and let U be a standard uniform random variable. a) Find a stochastic representation of the form (X₁, X₂) = (f(U), g(U)) for functions f and g which shows how (X₁, X2) can be simulated. b) With the help of a), use software and numerical integration to calculate the minimal correlation for two standard exponential random variables.
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- Let A be an n × n positive stochastic matrix withdominant eigenvalue λ1 = 1 and linearly independenteigenvectors x1, x2, . . . , xn, and let y0 be aninitial probability vector for a Markov chainy0, y1= Ay0, y2= Ay1, . . . Show that if y0 = c1x1 + c2x2 +· · ·+cnxn then the component c1 in the direction of the positive eigenvector x1 must be nonzero.A cellphone provider classifies its customers as low users (less than 400 minutes per month) or high users (400 or more minutes per month). Studies have shown that 80% of people who were low users one month will be low users the next month, and that 70% of the people who were high users one month will high users next month. a. Set up a 2x2 stochastic matrix with columns and rows labeled L and H that displays these transitions b. Suppose that during the month of January, 50% of the customers are low users. What percent of customers will be low users in February? In March?Let X and Y be two jointly Gaussian real random variables, each with zero mean,variance 1, and correlation coefficient ρ ∈ (0, 1). Let a, b ∈ R be such that a2 + b2 = 1, and defineW := aX + bY .a. Find values for a and b to maximize the variance of W . Hint: Use eigendecomposition.b. Does the optimal W (from part a) have a probability density function? If yes, derive it. Ifnot, explain why.
- If X1, X2, ... , Xn constitute a random sample of size n from an exponential population, show that X is a consis-tent estimator of the parameter θ.Prove the Markov inequality for continuous random variables with density f.The random variables ξ, ξ1, ξ2, . . . are independent and identically distributed with distribution P (ξ = 0) = 1/4 and P (ξ = j) = c/j for j = 1, 2, Let X0 = 0 and Xn = max(ξ1, . . . , ξn) for n = 1, 2, . . .. What value must c take? Explain why {Xn, n = 0, 1, 2,..... } is a Markov Write down the transition Draw the transition diagram and classify the states (aperiodic, transient, re- current, eorgodic, etc). Calculate P (Xn = 0). Calculate P (X4 = 3, X2 = 1|X1 = 3).
- X1 and X2 are independent random variables such that Xi has PDF fXi(x)={λiexp(−λix) when x≥0, 0 otherwise}. What is P[X2<X1]?Describe each of the five “Gauss Markov” assumptions, (define them) and explain in the context of the regression outputDefine the random process {Xt} by Xt = et + θ et−1. Show this process is weakly stationary.