Question

Consider a random sample X1, X2, ⋯ Xn from the pdf fx;θ=.51+θx, -1≤x≤1;0, o.w., where (this...

  1. Consider a random sample X1, X2, ⋯ Xn from the pdf

fx;θ=.51+θx, -1≤x≤1;0, o.w., where (this distribution arises in particle physics).

  1. Find the method of moment estimator of θ.

  1. Compute the variance of your estimator. Hint: Compute the variance of X and then apply the formula for X, etc.

Homework Answers

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Let X1, X2, . . . , Xn be iid random variables with pdf f(x|θ) =...
Let X1, X2, . . . , Xn be iid random variables with pdf f(x|θ) = θx^(θ−1) , 0 < x < 1, θ > 0. Is there an unbiased estimator of some function γ(θ), whose variance attains the Cramer-Rao lower bound?
Let X1,...,Xn be a random sample from the pdf f(x;θ) = θx^(θ−1) , 0 ≤ x...
Let X1,...,Xn be a random sample from the pdf f(x;θ) = θx^(θ−1) , 0 ≤ x ≤ 1 , 0 < θ < ∞ Find the method of moments estimator of θ.
Let X1,..., Xn be a random sample from a distribution with pdf as follows: fX(x) =...
Let X1,..., Xn be a random sample from a distribution with pdf as follows: fX(x) = e^-(x-θ) , x > θ 0 otherwise. Find the sufficient statistic for θ. Find the maximum likelihood estimator of θ. Find the MVUE of θ,θˆ Is θˆ a consistent estimator of θ?
Let X1, X2, ..., Xn be a random sample from a distribution with probability density function...
Let X1, X2, ..., Xn be a random sample from a distribution with probability density function f(x; θ) = (θ 4/6)x 3 e −θx if 0 < x < ∞ and 0 otherwise where θ > 0 . a. Justify the claim that Y = X1 + X2 + ... + Xn is a complete sufficient statistic for θ. b. Compute E(1/Y ) and find the function of Y which is the unique minimum variance unbiased estimator of θ. b.  Compute...
1. Let X1, X2, . . . , Xn be a random sample from a distribution...
1. Let X1, X2, . . . , Xn be a random sample from a distribution with pdf f(x, θ) = 1 3θ 4 x 3 e −x/θ , where 0 < x < ∞ and 0 < θ < ∞. Find the maximum likelihood estimator of ˆθ.
6. Let X1, X2, ..., Xn be a random sample of a random variable X from...
6. Let X1, X2, ..., Xn be a random sample of a random variable X from a distribution with density f (x)  ( 1)x 0 ≤ x ≤ 1 where θ > -1. Obtain, a) Method of Moments Estimator (MME) of parameter θ. b) Maximum Likelihood Estimator (MLE) of parameter θ. c) A random sample of size 5 yields data x1 = 0.92, x2 = 0.7, x3 = 0.65, x4 = 0.4 and x5 = 0.75. Compute ML Estimate...
Let {X1, ..., Xn} be i.i.d. from a distribution with pdf f(x; θ) = θ/xθ+1 for...
Let {X1, ..., Xn} be i.i.d. from a distribution with pdf f(x; θ) = θ/xθ+1 for θ > 2 and x > 1. (a) (10 points) Calculate EX1 and V ar(X1). (b) (5 points) Find the method of moments estimator of θ. (c) (5 points) If we denote the method of moments estimator as ˆθ1. What does √ n( ˆθ1 − θ) converge in distribution to? (d) (5 points) Is the method of moment estimator efficient? Verify your answer.
The random variable X is distributed with pdf fX(x, θ) = (2/θ^2)*x*exp(-(x/θ)2), where x>0 and θ>0....
The random variable X is distributed with pdf fX(x, θ) = (2/θ^2)*x*exp(-(x/θ)2), where x>0 and θ>0. Please note the term within the exponential is -(x/θ)^2 and the first term includes a θ^2. a) Find the distribution of Y = (X1 + ... + Xn)/n where X1, ..., Xn is an i.i.d. sample from fX(x, θ). If you can’t find Y, can you find an approximation of Y when n is large? b) Find the best estimator, i.e. MVUE, of θ?
Let X1, X2 · · · , Xn be a random sample from the distribution with...
Let X1, X2 · · · , Xn be a random sample from the distribution with PDF, f(x) = (θ + 1)x^θ , 0 < x < 1, θ > −1. Find an estimator for θ using the maximum likelihood
6. Let θ > 1 and let X1, X2, ..., Xn be a random sample from...
6. Let θ > 1 and let X1, X2, ..., Xn be a random sample from the distribution with probability density function f(x; θ) = 1/(xlnθ) , 1 < x < θ. a) Obtain the maximum likelihood estimator of θ, ˆθ. b) Is ˆθ a consistent estimator of θ? Justify your answer.