Question

In: Statistics and Probability

Let X1,...,Xn be a random sample from a gamma distribution with shape parameter α and rate...

Let X1,...,Xn be a random sample from a gamma distribution with shape parameter α and rate β (note that this may be a different gamma specification than you are used to). Then

f(x | α, β) = (βα/Γ(α))*x^(α−1) * e^(−βx). where x, α, β > 0

(a) Derive the equations that yield the maximum likelihood estimators of α and β. Can they be solved explicitly? Hint: don’t forget your maximum checks, and it may help to do some internet searching for the “digamma function”, including plotting for possible values...

(b) Find jointly sufficient statistics for α and β.

Solutions

Expert Solution


Related Solutions

Let X1, … , Xn. be a random sample from gamma (2, theta) distribution. a) Show...
Let X1, … , Xn. be a random sample from gamma (2, theta) distribution. a) Show that it is the regular case of the exponential class of distributions. b) Find a complete, sufficient statistic for theta. c) Find the unique MVUE of theta. Justify each step.
2. Let X1, . . . , Xn be a random sample from the distribution with...
2. Let X1, . . . , Xn be a random sample from the distribution with pdf given by fX(x;β) = β 1(x ≥ 1). xβ+1 (a) Show that T = ni=1 log Xi is a sufficient statistic for β. Hint: Use n1n1n=exp log=exp −logxi .i=1 xi i=1 xi i=1 (b) Find the pdf of Y = logX, where X ∼ fX(x;β). (c) Find the distribution of T . Hint: Identify the distribution of Y and use mgfs. (d) Find...
Let X1, . . . , Xn be a random sample from a uniform distribution on...
Let X1, . . . , Xn be a random sample from a uniform distribution on the interval [a, b] (i) Find the moments estimators of a and b. (ii) Find the MLEs of a and b.
) Let X1, . . . , Xn be iid from the distribution with parameter η...
) Let X1, . . . , Xn be iid from the distribution with parameter η and probability density function: f(x; η) = e ^(−x+η) , x > η, and zero otherwise. 1. Find the MLE of η. 2. Show that X_1:n is sufficient and complete for η. 3. Find the UMVUE of η.
Let X1,X2, . . . , Xn be a random sample from the uniform distribution with...
Let X1,X2, . . . , Xn be a random sample from the uniform distribution with pdf f(x; θ1, θ2) = 1/(2θ2), θ1 − θ2 < x < θ1 + θ2, where −∞ < θ1 < ∞ and θ2 > 0, and the pdf is equal to zero elsewhere. (a) Show that Y1 = min(Xi) and Yn = max(Xi), the joint sufficient statistics for θ1 and θ2, are complete. (b) Find the MVUEs of θ1 and θ2.
Let X1,..., Xn be an i.i.d. sample from a geometric distribution with parameter p. U =...
Let X1,..., Xn be an i.i.d. sample from a geometric distribution with parameter p. U = ( 1, if X1 = 1, 0, if X1 > 1) find a sufficient statistic T for p. find E(U|T)
in R. Generate a random sample of size 700 from a gamma distribution with shape parameter...
in R. Generate a random sample of size 700 from a gamma distribution with shape parameter 8 and scale parameter 0.1. Create a histogram of the sample data. Draw the true parametric density (for the specified gamma distribution) on the histogram. The curve for the density should be red. (Note: The “true parametric density” is the distribution from which the sample values were generated. It is NOT the kernel density that is estimated from the data.)
Suppose X1, X2, ..., Xn is a random sample from a Poisson distribution with unknown parameter...
Suppose X1, X2, ..., Xn is a random sample from a Poisson distribution with unknown parameter µ. a. What is the mean and variance of this distribution? b. Is X1 + 2X6 − X8 an estimator of µ? Is it a good estimator? Why or why not? c. Find the moment estimator and MLE of µ. d. Show the estimators in (c) are unbiased. e. Find the MSE of the estimators in (c). Given the frequency table below: X 0...
Let X1, X2, . . . , Xn represent a random sample from a Rayleigh distribution...
Let X1, X2, . . . , Xn represent a random sample from a Rayleigh distribution with pdf f(x; θ) = (x/θ) * e^x^2/(2θ) for x > 0 (a) It can be shown that E(X2 P ) = 2θ. Use this fact to construct an unbiased estimator of θ based on n i=1 X2 i . (b) Estimate θ from the following n = 10 observations on vibratory stress of a turbine blade under specified conditions: 16.88 10.23 4.59 6.66...
2. Let X1, ..., Xn be a random sample from a uniform distribution on the interval...
2. Let X1, ..., Xn be a random sample from a uniform distribution on the interval (0, θ) where θ > 0 is a parameter. The prior distribution of the parameter has the pdf f(t) = βαβ/t^(β−1) for α < t < ∞ and zero elsewhere, where α > 0, β > 0. Find the Bayes estimator for θ. Describe the usefulness and the importance of Bayesian estimation. We are assuming that theta = t, but we are unsure if...
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT