6. Let {Xn, n N} be a sequence of independent random variables exponentially distributed with param- eter λ=1. The law of large numbers (LLN) states that: AP(Xi − 2 > e) → 0 for any e > 0 as n → ∞. - X₁ [B]P(|¹ − | > e) → 0 for any € > 0 as n → ∞. C (-2)) → Z, in distribution as n → ∞, where Z is a standard normal. D (²1(x-¹)) → Z, in distribution as n→ ∞o, where Z is a standard normal.
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- Let X1,X2,... be a sequence of identically distributed random variables with E|X1|<∞ and let Yn = n−1max1≤i≤n|Xi|. Show that limnE(Yn) = 0Let X1, X2, X3, . . . be a sequence of independent Poisson distributed random variables with parameter 1. For n ≥ 1 let Sn = X1 + · · · + Xn. (a) Show that GXi(s) = es−1.(b) Deduce from part (a) that GSn(s) = ens−n.Let Xi be IID random variables which have the same law as X. Let L(t) = E(e^tX.) Suppose that this is well defined for t ∈ [−1, 1]. Express the moment generating function of the Sum from i=1 to k Xi in terms of k and L
- Suppose X1, X2, ... , Xn is a random sample and Xi = {1, with probability p 0, with probability 1-p} for every i = 1, 2, ... , n. Find the Moment Generating Function of ∑i=1n Xi . What is the distribution of ∑i=1n Xi ?If a random variable X has a discrete uniform distribution. fx(x)=1/k for x=1,2,..,k;0 otherwise. Derive P.G.F of X and compute E(2x+1)Let X1, X2, ... , Xn be a random sample from N(μ, σ2). Find the Moment Generating Function of X̅. If n = 16 and σ = 2, compute P(-1 ≤ X̅ - μ ≤ 1).
- Let Xi be arandom sample from U(0,1)prove that Xn’ convarges in probability to 0.50LetX1,X2,...,Xn be a sequence of independent and identically distributed random variables having the Exponential(λ) distribution,λ >0, fXi(x) ={λe−λx, x >0 0, otherwise Define the random variable Y=X1+X2+···+Xn. Find E(Y),Var(Y)and the moment generating function ofY.LetX1,X2,...,Xn be a sequence of independent and identically distributed random variables having the Exponential(λ) distribution,λ >0, fXi(x) ={λe−λx, x >0 0, otherwise (a) Show that the moment generating function mX(s) :=E(esX) =λ/(λ−s) for s< λ;
- Suppose that {Xn, n ≥ 1} is a sequence of discrete random variables with distribution functionP(Xn =i/n) = 2i/(n(n + 1)), for i = 1, 2, ..., n.Let X be a continuous random variable with density functionfX(x) = 2x for 0 < x < 1,0 , otherwise(a) Compute P(X ≤ x) for 0 < x < 1;(b) Compute P(Xn ≤m/n) for some integer m such that 1 ≤ m ≤ n;(c) Compute P(Xn ≤ x) for 0 < x < 1;(d) Prove that Xnd→ X.Let i_t denote the effective annual return achieved on an equity fund achieved between time (t -1) and time t. Annual log-returns on the fund, denoted by In(1 + i_t) , are assumed to form a series of independent and identically distributed Normal random variables with parameters u = 6% and o = 14%.An investor has a liability of £10,000 payable at time 15. Calculate the amount of money that should be invested now so that the probability that the investor will be unable to meet the liability as it falls due is only 5%. Using only formulas, no tablesShow that the random process X(t) =cos(2π fot + θ) Where θ is an random variable uniformly distributed in the range {0, π/2, π, π/3} is a wide sense stationary process .