Please rate
X, be a random sample from a distribution with the probability density function f(x; θ) =...
4. Let Xi, X2, ensity function f(r; , Xn be a random sample from a distribution with the probability θ)-(1/2)e-11-01,-oo <エく00,-00 < θ < oo. Find the d MLE θ
3. Let X1, X2, . . . , Xn be a random sample from a distribution with the probability density function f(x; θ) (1/02)Te-x/θ. O < _T < OO, 0 < θ < 00 . Find the MLE θ
2. Let Xi, X2, . Xn be a random sample from a distribution with the probability density function f(x; θ-829-1, 0 < x < 1,0 < θ < oo. Find the MLE θ
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability density function f(x; θ) = (1/2)e-11-01, o < x < oo,-oo < θ < oo. Find the NILE θ.
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability density function f(x; θ) = (1/2)e-11-01, o < x < oo,-oo < θ < oo. Find the NILE θ.
Let X1, X2,.. Xn be a random sample from a distribution with probability density function f(z | θ) = (g2 + θ) 2,0-1(1-2), 0<x<1.0>0 obtain a method of moments estimator for θ, θ. Calculate an estimate using this estimator when x! = 0.50. r2 = 0.75, хз = 0.85, x4= 0.25.
density function f(x; θ)-829-1, 0 < x < 1, 0 < θ < oo. Find the MLE θ
3. (a) Suppose that ri,...,In are a random sample having probability density function C: a Here α is restricted to be positive. Determine the MLE of α (b) Suppose that ri, , Vn are a random sample from a geometric distribution ㄨㄧ Here the parameter 0 < θ < I. Determine the MLE of θ and show carefully that it is an MLE: it does not suffice to solve the score equation.
3. (a) Suppose that xi,... ,Vn are a random sample having probability density function Here α is restricted to be positive. Determine the MLE of a. (b) Suppose that r1,..., Jn are a random sample from a geometric distribution Here the parameter 0 < θ < 1. Determine the MLE of θ and show carefully that it is an MLE: it does not suffice to solve the score equation
Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-re, x > 0 ) is sufficient for θ, using x/θ the definition ofsuficiency. Then show that the mle of θ is a function of the sufficient statistic. Show that the sum of the observations of a random sample of size n from gamma distribution with parameters 1 and θ (so f(x:0)-e-re, x > 0 ) is...