Suppose we are analyzing data from the exponential distribution, which has density function f (y) =...
QUESTION 4 Suppose Xis a random variable with probability density function f(x) and Y is a random variable with density function f,(x). Then X and Y are called independent random variables if their joint density function is the product of their individual density functions: x, y We modelled waiting times by using exponential density functions if t <0 where μ is the average waiting time. In the next example we consider a situation with two independent waiting times. The joint...
Exponential(). That is Y has a density function of the form 7. Let Y Ay f(y) = de"^9,y> 0 where 0. Show that: (a) If a >0 and b > 0, then P(Y > a + b|Y > a) = P(Y > b) (b) E(Y) 1/A
Let X1, X2,.,X10 be a sample of size 10 from an exponential distribution with the density function Sae -Xx f(x; A) otherwise 10 We reject Ho : ^ = 1 in favor of H : 1 = 2 if the observed value of Y = smaller than 6 (a) Find the probability of type 1 error for this test. (b) Find the probability of type 2 error for this test (c) Let y5 be the observed value of Y. Find...
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...
Let random variables X and Y have the bi-variate exponential CDF (cumulative distribution function) : F(x,y) = 1 - exp(-x) - exp(-y) + exp(-x-y-xy) Given x > 0, y>0 a) Determine the probability that 4 < X given that Y = 2 b) Determine the probability that 4 < X given that Y is less than or equal to 2
Return to the original model. We now introduce a Poisson intensity parameter X for every time point and denote the parameter () that gives the canonical exponential family representation as above by θ, . We choose to employ a linear model connecting the time points t with the canonical parameter of the Poisson distribution above, i.e., n other words, we choose a generalized linear model with Poisson distribution and its canonical link function. That also means that conditioned on t,...
Suppose Y follows a distribution with density function: f(y)={101846(100–y)? if(0<y<100) otherwise (Note: for this question you can enter your answer in decimals as well as fractions. You can round to two decimal places as well) 1. What must the value of k be so that f(x)is a probability density function? Submit Answer Tries 0/5 Based on this model, which event is more likely? A. a person dies between the ages of 70 and 80. B. a person lives beyond age...
Let > 0 and let X1, X2, ..., Xn be a random sample from the distribution with the probability density function f(x; 1) = 212x3e-dız?, x > 0. a. Find E(X), where k > -4. Enter a formula below. Use * for multiplication, / for divison, ^ for power, lam for \, Gamma for the function, and pi for the mathematical constant 11. For example, lam^k*Gamma(k/2)/pi means ik r(k/2)/ I. Hint 1: Consider u = 1x2 or u = x2....
Let > 0 and let X1, X2, ..., Xn be a random sample from the distribution with the probability density function f(x; 1) = 212x3 e-tz, x > 0. a. Find E(XK), where k > -4. Enter a formula below. Use * for multiplication, / for divison, ^ for power, lam for 1, Gamma for the function, and pi for the mathematical constant i. For example, lam^k*Gamma(k/2)/pi means ik r(k/2)/n. Hint 1: Consider u = 1x2 or u = x2....