Question

   Let {Xi} be i.i.d. random variables with P(Xi=−1) = P(Xi= 1) = 1/2. Let Sn=...

  

Let {Xi} be i.i.d. random variables with P(Xi=−1) = P(Xi= 1) = 1/2. Let Sn= 1 +X1+. . .+Xn be symmetric simple random walk with initial point S0 = 1. Find the probability that Sn eventually hits the point 0.

Hint: Define the events A={Sn= 0 for some n} and for M >1, AM = {Sn hits 0 before hitting M}.

Show that AM ↗ A.

Homework Answers

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
X1, … Xn are i.i.d. random variables, and E(Xi ) = 3β, Var(Xi ) = 3β^2...
X1, … Xn are i.i.d. random variables, and E(Xi ) = 3β, Var(Xi ) = 3β^2 , i = 1 … n, β > 0. Two estimators of β are defined as β̂ 1 = (X̅ /3) β̂ 2 = (n /3n+1 ) X̅ Show that MSE(β̂ 2) < MSE(β̂ 1) for a sample size of n = 3.
Let {Xj} ∞ j=1 be a collection of i.i.d. random variables uniformly distributed on [0, 1]....
Let {Xj} ∞ j=1 be a collection of i.i.d. random variables uniformly distributed on [0, 1]. Let N be a Poisson random variable with mean n, and consider the random points {X1 , . . . , XN }. b. Let 0 < a < b < 1. Let C(a,b) be the number of the points {X1 , . . . , XN } that lies in (a, b). Find the conditional mass function of C(a,b) given that N =...
Honest data is repeatedly released independently. Let Xi be the result of i-th launch and Sn...
Honest data is repeatedly released independently. Let Xi be the result of i-th launch and Sn = X1 + X2,. . . , Xn, obtain: a) lim→∞ P(Sn> 3n). b) An approximate value for P (S100> 320) Answers: a)1; b)0,96
Let X1, X2, ... be i.i.d. r.v. and N an independent nonnegative integer valued r.v. Let...
Let X1, X2, ... be i.i.d. r.v. and N an independent nonnegative integer valued r.v. Let SN=X1 +...+ XN. Assume that the m.g.f. of the Xi, denoted MX(t), and the m.g.f. of N, denoted MN(t) are finite in some interval (-δ, δ) around the origin. 1. Express the m.g.f. MS_N(t) of SN in terms ofMX(t) and MN(t). 2. Give an alternate proof of Wald's identity by computing the expectation E[SN] as M'S_N(0). 3. Express the second moment E[SN2] in terms...
5. Let X1, X2, . . . be independent random variables all with mean E(Xi) =...
5. Let X1, X2, . . . be independent random variables all with mean E(Xi) = 7 and variance Var(Xi) = 9. Set Yn = X1 + X2 + · · · + Xn n (n = 1, 2, 3, . . .) (a) Find E(Y2) and E(Y5). (b) Find Cov(Y2, Y5). (c) Find E (Y2 | X1). (d) How should your answers from parts (a)–(c) be modified if the numbers “2”, “5”, “7” and “9” are replaced by m,...
Let Xi, i = 1, 2..., 48, be independent random variables that are uniformly distributed on...
Let Xi, i = 1, 2..., 48, be independent random variables that are uniformly distributed on the interval [-0.5, 0.5]. (a) Find the probability Pr(|X1|) < 0.05 (b) Find the approximate probability P (|Xbar| ≤ 0.05). (c) Determine an approximation of a such that P(Xbar ≤ a) = 0.15
(14pts) Let X and Y be i.i.d. geometric random variables with parameter (probability of success) p,...
(14pts) Let X and Y be i.i.d. geometric random variables with parameter (probability of success) p, 0 < p < 1. (a) (6pts) Find P(X > Y ). (b) (8pts) Find P(X + Y = n) and P(X = k∣X + Y = n), for n = 2, 3, ..., and k = 1, 2, ..., n − 1.
2 Let X1,…, Xn be a sample of iid NegBin(4, ?) random variables with Θ=[0, 1]....
2 Let X1,…, Xn be a sample of iid NegBin(4, ?) random variables with Θ=[0, 1]. Determine the MLE ? ̂ of ?.
(1 point)   Let XiXi for i=1,2,3,… be a random variable whose probability distribution has an average...
(1 point)   Let XiXi for i=1,2,3,… be a random variable whose probability distribution has an average of μ=15 and a standard deviation of σ=2. Assume the Xi are independent and continuous. Let Sn=X1+⋯+Xn To use a Normal distribution to approximate P(S49<750), find the area to the left of 750 under a Normal curve with center (average) and spread (standard deviation)  . The estimated probability is
Let X1, . . . , Xn be i.i.d from pmf f(x|λ) where f(x) = (e^(−λ)*(λ^x))/x!,...
Let X1, . . . , Xn be i.i.d from pmf f(x|λ) where f(x) = (e^(−λ)*(λ^x))/x!, λ > 0, x = 0, 1, 2 a) Find MoM (Method of Moments) estimator for λ b) Show that MoM estimator you found in (a) is minimal sufficient for λ c) Now we split the sample into two parts, X1, . . . , Xm and Xm+1, . . . , Xn. Show that ( Sum of Xi from 1 to m, Sum...