Suppose that x1, . . . , xn are a random sample of lifetimes for individuals diagnosed with a certain disease. Assume a model with f(x; λ) = k(x/λ)^k−1 * e^−(x/λ)^k /λ, x > 0, where k is fixed and known. Interest is in the parameter
ζ = P(X > 25; λ) which gives the probability that an individual will survive more than 25 years with the disease. It can be shown that the cumulative distribution is F(x; λ) = P(X ≤ x; λ) = e^−(x/λ)^k .
1. Determine the MLE of ζ.
Suppose that x1, . . . , xn are a random sample of lifetimes for individuals...
(b) Suppose that xi, . . . ,Xn are a random sample of lifetimes for individuals diagnosed with a certain disease. Assume a model with λ, x>0, where k is fixed and known Interest is in the parameter Ç P(X > 25A) which gives the probability that an individual will survive more than 25 years with the disease. It can be shown that the cumulative distribution Determine the MLE of
Suppose that xı,... , In are a random sample of lifetimes for individuals diagnosed with a certain disease. Assume a model with where k is fixed and known Interest is in the parameter -P(X> 25; A) which gives the probability that an individual will survive more than 25 years with the disease. It can be shown that the cumulative distribution Determine the MLE of
2. (a) Two objects with unknown weights μ1 and μ2 are weighed separately on a scale and together (both placed on the scale), giving three measurements X1 = 15.6, T2 = 29.3 and 23 = 458 It is known from previous experience with the scale that measurements are independent and normally distributed about the true weights with variance 1. Determine the maximum likelihood estimates of u1 and 2 (b) Suppose that r1, ,Tn are a random sample of lifetimes for...
2. (a) Two objects with unknown weights μ1 and μ2 are weighed separately on a scale and together (both placed on the scale), giving three measurements15.6,r2 29.3 and r 45.8. It is known from previous experience with the scale that measurements are independent and normally distributed about the true weights with variance 1 Determine the maximuin likelihood estimates of 111 and μ2. (b) Suppose that ri,..., In are a random sample of lifetimes for individuals diagnosed with a certain disease....
Suppose X1, X2, · · · , Xn form a random sample from a distribution with p.d.f. f(x;?)=(1+?)x?, 0<x<1, ?>0. a. Find the MLE of ?. b. Show that the MLE is sufficient for ?.
7. Let X1,....Xn random sample from a Bernoulli distribution with parameter p. A random variable X with Bernoulli distribution has a probability mass function (pmf) of with E(X) = p and Var(X) = p(1-p). (a) Find the method of moments (MOM) estimator of p. (b) Find a sufficient statistic for p. (Hint: Be careful when you write the joint pmf. Don't forget to sum the whole power of each term, that is, for the second term you will have (1...
Let Ņ, X1. X2, . . . random variables over a probability space It is assumed that N takes nonnegative inteqer values. Let Zmax [X1, -. .XN! and W-min\X1,... ,XN Find the distribution function of Z and W, if it suppose N, X1, X2, are independent random variables and X,, have the same distribution function, F, and a) N-1 is a geometric random variable with parameter p (P(N-k), (k 1,2,.)) b) V - 1 is a Poisson random variable with...
Suppose that X1,..., Xn is a random sample from a gamma distribu- tion, The gamma distribution has parameters r and λ, and also has E(X)-r/λ and Var(X)-r/ P. Calculate the method of moments MOM) estimators of r and λ in terms of the first two sample moments Mi and M2
Suppose that X1,..., Xn is a random sample from a gamma distribu- tion, The gamma distribution has parameters r and λ, and also has E(X)-r/λ and Var(X)-r/ P. Calculate the...
2. a. Letỉ be the median of X1, , xn, n odd. Prove that the identity 1-1 1-1 Hold if and only if z b. Let X1, , Xn be a random sample form f(p, b), where f(p, b) is the Laplace distribution with density 1 2h2 -k-시 Assumingthat b is known and that n is odd, show that the MLE of μ is the sample median, X. (Hint: Use (a).)
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.