5. The Exponential(A) distribution has density f(x) = for x<0' where λ > 0 (a) Show/of(x)...
(c) Let X have an exponential density with parameter λ > 0, Prove the "memoryless" property: P(X > t + s|X > s) = P(X > t) for t>0 and s 0. For example, the probability that the conversation lasts at least t more minutes is the same as the probability of it lasting at least t minutes in the first place.
6. Let X have exponential density f(x) = le-Az if x > 0, f(x) = 0 otherwise (>0). Compute the moment-generating function of X.
Suppose X has an exponential distribution with parameter λ = 1 and Y |X = x has a Poisson distribution with parameter x. Generate at least 1000 random samples from the marginal distribution of Y and make a probability histogram.
1. Suppose that Xi,..,Xn are independent Exponential random variables with density f(x; λ) λ exp(-1x) for x > 0 where λ > 0 is an unknown parameter (a) Show that the τ quantile of the Exponential distribution is F-1 (r)--X1 In(1-7) and give an approximation to Var(X(k)) for k/n-T. What happens to this variance as τ moves from 0 to 1? (b) The form of the quantile function in part (a) can be used to give a quantile-quantile (QQ) plot...
1. Suppose that Xi,..,Xn are independent Exponential random variables with density f(x; λ) λ exp(-1x) for x > 0 where λ > 0 is an unknown parameter (a) Show that the τ quantile of the Exponential distribution is F-1 (r)--X1 In(1-7) and give an approximation to Var(X(k)) for k/n-T. What happens to this variance as τ moves from 0 to 1? (b) The form of the quantile function in part (a) can be used to give a quantile-quantile (QQ) plot...
Recall that X ∼ Exp(λ) if the probability density function of X is fX(x) = λe−λx for x ≥ 0. Let X1, . . . , Xn ∼ Exp(λ), where λ is an unknown parameter. Exponential random variables are often used to model the time between rare events, in which case λ is interpreted as the average number of events occurring per unit of time. Recall that X ~ Exp(A) if the probability density function of X is fx(x)-Ae-Az for...
Consider a distribution with parameter λ >0 that has density f(x)= x^4/(24 λ^5) e^(-x/λ). You test the hypothesesH0:test λ =1 vs λ ≠ 1 by using the test Test: I Xbar-5 I > c. Find the smallest threshold C for an asymptotic level α
3. If X is an exponential random variable with parameter λ > 0, show that for c > 0 cX is exponential with parameter λ/c.
1. Let {x, t,f 0) and {Yǐ.12 0) be independent Poisson processes,with rates λ and 2A, respectively. Obtain the conditionafdistributiono) Moreover, find EX Y X2t t given Yt-n, n = 1,2. 2, (a) Let T be an exponential random variable with parameter θ. For 12 0, compute (b) When Amelia walks from home to work, she has to cross the street at a certain point. Amelia needs a gap of a (units of time) in the traffic to cross the...
In question 5, f(x) = λ*exp(-λx), for x greater or equal to 0, and zero otherwise. 9. Let X have an exponential distribution with λ = 1 (see Question 5), and let Y = log(X). Find the probability density function of Y. Where is the density non-zero? Note that in this course, log refers to the log base e, or natural log, often symbolized In. The distribution of Y is called the (standard) Gumbel, or extreme value distribution. 2