Question

Suppose that (X1, · · · , Xn) is a random sample from uniform distribution U(0,...

Suppose that (X1, · · · , Xn) is a random sample from uniform distribution U(0, θ).

(a) Prove that T(X1, · · · , Xn) = X(n) is minimal sufficient for θ. (X(n) is the largest order statistic, i.e., X(n) = max{X1, · · · , Xn}.)

(b) In addition, we assume θ ≥ 1. Find a minimal sufficient statistic for θ and justify your answer.

Homework Answers

Answer #1

*** if you have any queries or doubts please comment below. If you're satisfied please give a like. Thank you!

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
Suppose that X1,...,Xn ∼ U(0,θ); that is, a sample of N observations from a random variable...
Suppose that X1,...,Xn ∼ U(0,θ); that is, a sample of N observations from a random variable with a uniform distribution where the lower bound is 0 and the upper bound θ is unknown. Find the maximum likelihood estimate of θ, also demonstrating this in R. Draw the pdf and the likelihood, and explain what they represent, in R.
Suppose that X1,..., Xn form a random sample from the uniform distribution on the interval [0,θ],...
Suppose that X1,..., Xn form a random sample from the uniform distribution on the interval [0,θ], where the value of the parameter θ is unknown (θ>0). (1)What is the maximum likelihood estimator of θ? (2)Is this estimator unbiased? (Indeed, show that it underestimates the parameter.)
Let X2, ... , Xn denote a random sample from a discrete uniform distribution over the...
Let X2, ... , Xn denote a random sample from a discrete uniform distribution over the integers - θ, - θ + 1, ... , -1, 0, 1, ... ,  θ - 1,  θ, where  θ is a positive integer. What is the maximum likelihood estimator of  θ? A) min[X1, .. , Xn] B) max[X1, .. , Xn] C) -min[X1, .. , Xn​​​​​​​] D) (max[X1, .. , Xn​​​​​​​] - min[X1, .. , Xn​​​​​​​]) / 2 E) max[|X1| , ... , |Xn|]
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...
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 an exponential distribution...
Let X1, X2, · · · , Xn be a random sample from an exponential distribution f(x) = (1/θ)e^(−x/θ) for x ≥ 0. Show that likelihood ratio test of H0 : θ = θ0 against H1 : θ ≠ θ0 is based on the statistic n∑i=1 Xi.
Let X1, . . . , Xn be a random sample from a Poisson distribution. (a)...
Let X1, . . . , Xn be a random sample from a Poisson distribution. (a) Prove that Pn i=1 Xi is a sufficient statistic for λ. (b) The MLE for λ in a Poisson distribution is X. Use this fact and the result of part (a) to argue that the MLE is also a sufficient statistic for λ.
Let X1, X2, · · · , Xn be a random sample from the distribution, f(x;...
Let X1, X2, · · · , Xn be a random sample from the distribution, f(x; θ) = (θ + 1)x^ −θ−2 , x > 1, θ > 0. Find the maximum likelihood estimator of θ based on a random sample of size n above
Suppose X1 ...... Xn is a random sample from the uniform distribution on [a; b]. (a)...
Suppose X1 ...... Xn is a random sample from the uniform distribution on [a; b]. (a) Find the method of moments estimators of a and b. (b) Find the maximum likelihood estimators of a and b. please step by step
Let X1, X2, . . . , Xn be a random sample from a population following...
Let X1, X2, . . . , Xn be a random sample from a population following a uniform(0,2θ) (a) Show that if n is large, the distribution of Z =( X − θ) / ( θ/√ 3n) is approximately N(0, 1). (b) We can estimate θ by X(sample mean) and define W = (X − θ ) / (X/√ 3n) which is also approximately N(0, 1) for large n. Derive a (1 − α)100% confidence interval for θ based on...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT