You are given the exponential distribution defined for x 〉 0 and λ 〉 0, Mathematically...
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.
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...
Suppose that X has an exponential distribution with parameter λ. Find the pdf of X2
5. The Exponential(A) distribution has density f(x) = for x<0' where λ > 0 (a) Show/of(x) dr-1. (b) Find F(x). Of course there is a separate answer for x 2 0 and x <0 (c Let X have an exponential density with parameter λ > 0 Prove the 'Inemoryless" 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...
7. (15 pts) Suppose X1, X2, ..., X, is a random sample from an exponential distribution with parameter 2. (Remember f(x;2) = ne-^x is the pdf for the exponential dista.) a) Find the likelihood function, L(X1, X2, Xn). b) Find the log-likelihood function, I = log L. c) Find d //d, set the result = 0 and solve for 2.
An exponential distribution with unknown parameter λ=θλ=θ is sampled four times, yielding the values 4.1,0.8,0.6,34.1,0.8,0.6,3. Find each of the following. (Write theta for θ.) (a) The likelihood function L(θ)= (b) The derivative of the log-likelihood function =d/dθ[lnL(θ)]= (c) The maximum likelihood estimate for θ is θ̂ =
The Poisson distribution with parameter λ has the mass function defined by p(x) = λ x e −λ/x! if x is a nonnegative integer (and 0 otherwise). Find the probability it assigns to each of the following sets: a. [0, 2) b. (−∞,1] c. (−∞,1.5] d. (−∞, 2) e. (−∞,2] f. (0.5, ∞) g. {0, 1, 2} Find the CDF of the uniform distribution on (0,1).
7. Suppose X1, X2, ..., Xn is a random sample from an exponential distribution with parameter K. (Remember f(x;2) = 2e-Ax is the pdf for the exponential dist”.) a) Find the likelihood function, L(X1, X2, ..., Xn). b) Find the log-likelihood function, b = log L. c) Find dl/d, set the result = 0 and solve for 2.
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...