Let X1, X2, ..., Xn be iid with pdf f(x|θ) = θ*x(θ-1). a) Find the Maximum Likelihood Estimator of θ, and b) show that its variance converges to 0 as n approaches infinity.
I have no problem with part a, finding the MLE of θ. However, I'm having some trouble with finding the variance.
The professor walked us through part b generally, but I need help with univariate transformation for sigma(-ln(xi)) (see picture below - the professor used Y=sigma(-ln(x)), and w=sigma(Yi). I need help particularly with the w=signma(Yi) transformation.

Let X1, X2, ..., Xn be iid with pdf f(x|θ) = θ*x(θ-1). a) Find the Maximum...
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Suppose thatX1,...Xn are IID with pdf f(x;θ) = 1 /2θ if -θ<x<θ otherwise =0 (a) Find an unbiased estimator of θ. You must prove that your estimator is unbiased. (b) Find the variance of the estimator in (a).
Suppose X1, X2, ..., Xn is an iid sample from fx(r ja-θ(1-z)0-11(0 1), where x θ>0. (a) Find the method of moments (MOM) estimator of θ. (b) Find the maximum likelihood estimator (MLE) of θ (c) Find the MLE of Po(X 1/2) d) Is there a function of θ, say T 0), for which there exists an unbiased estimator whose variance attains the Cramér-Rao Lower Bound? If so, find it and identify the corresponding estimator. If not, show why not.
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Let X1, ..., Xn be IID observations from Uniform(0, θ). T(X) = max(X1, . . . Xn) is a sufficient statistic (additionally, T is the MLE for θ). Find a (1 − α)-level confidence interval for θ. [Note: The support of this distribution changes depending on the value of θ, so we cannot use Fisher’s approximation for the MLE because not all of the regularity assumptions hold.]
Suppose that X1, X2, ,Xn is an iid sample from Íx (x10), where θ Ε Θ. In each case below, find (i) the method of moments estimator of θ, (ii) the maximum likelihood estimator of θ, and (iii) the uniformly minimum variance unbiased estimator (UMVUE) of T(9) 0. exp fx (x10) 1(0 < x < 20), Θ-10 : θ 0}, τ(0) arbitrary, differentiable 20 (d) n-1 (sample size of n-1 only) ー29 In part (d), comment on whether the UMVUE...
Let X1, X2,...,Xn denote a random sample from a distribution
that is N(0, θ).
a) Show that Y = sigma (1 to n) Xi2 is a complete
sufficient statistic for θ. (solved)
b) Find the UMVUE of θ2. (need help with this
one)
Note: I am in particular having trouble finding out what
distribution Y = sigma Xi^2 is. The professor advise us to find the
second moment generating function for Y, but I not sure how I find
that....
Let X1, X2, . . . , Xn iid∼ Exponential(β). The probability density function for these random variables is f(x|β) = 1 β e − x β , x > 0; β > 0. a. Find the maximum likelihood estimator for β. b. Compare the MLE to the estimator βˆ = (X1 + Xn)/2. Which do you recommend? Explain. You can use the following facts: E(Xi) = β and V ar(Xi) = β 2 , for any i. c. Construct...
As on the previous page, let X1,... ,Xn be iid with pdf where θ > 0. (to) 2 Possible points (qualifiable, hidden results) Assume we do not actually get to observe Xı , . . . , X. . Instead let Yı , . . . , Y, be our observations where Yi = 1 (Xi 0.5) . Our goal is to estimate 0 based on this new data. What distribution does Y follow? First, choose the type of distribution:...
Let X1 Xn be a random sample from a distribution with the pdf f(x(9) = θ(1 +0)-r(0-1) (1-2), 0 < x < 1, θ > 0. the estimator T-4 is a method of moments estimator for θ. It can be shown that the asymptotic distribution of T is Normal with ETT θ and Var(T) 0042)2 Apply the integral transform method (provide an equation that should be solved to obtain random observations from the distribution) to generate a sam ple of...