Question

Let Y1,Y2,Y3,...,Yn denote a random sample from a Poisson probability distribution. (a) Show that sample mean,...

Let Y1,Y2,Y3,...,Yn denote a random sample from a Poisson probability distribution.

(a) Show that sample mean, ˆ θ1 = ¯ Y and the sample variance, ˆ θ2 = S2 are both unbiased estimators of θ.

(b) Calculate relative efficiency of the two estimators in (a). Based on your calculation, Which of the two estimators in 3a would you select as a better estimator?

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 Y1, Y2, Y3 ,..,, Yn be a random sample from a normal distribution with mean...
Let Y1, Y2, Y3 ,..,, Yn be a random sample from a normal distribution with mean µ and standard deviation 1. Then find the MVUE( Minimum - Variance Unbiased Estimation) for the parameters: µ^2 and µ(µ+1)
Let Y1, Y2, . . . , Yn denote a random sample from a uniform distribution...
Let Y1, Y2, . . . , Yn denote a random sample from a uniform distribution on the interval (0, θ). (a) (5 points)Find the MOM for θ. (b) (5 points)Find the MLE for θ.
Let Y1, Y2, . . ., Yn be a random sample from a Laplace distribution with...
Let Y1, Y2, . . ., Yn be a random sample from a Laplace distribution with density function f(y|θ) = (1/2θ)e-|y|/θ for -∞ < y < ∞ where θ > 0. The first two moments of the distribution are E(Y) = 0 and E(Y2) = 2θ2. a) Find the likelihood function of the sample. b) What is a sufficient statistic for θ? c) Find the maximum likelihood estimator of θ. d) Find the maximum likelihood estimator of the standard deviation...
Let Y1,Y2,...,Yn denote a random sample of size n from a population with a uniform distribution...
Let Y1,Y2,...,Yn denote a random sample of size n from a population with a uniform distribution on the interval (0,θ). Let Y(n)= max(Y1,Y2,...,Yn) and U = (1/θ)Y(n) . a) Show that U has cumulative density function 0 ,u<0, Fu (u) =   un ,0≤u≤1, 1 ,u>1
Let Y1, Y2, . . ., Yn be a random sample from a uniform distribution on...
Let Y1, Y2, . . ., Yn be a random sample from a uniform distribution on the interval (θ - λ, θ + λ) where -∞ < θ < ∞ and λ > 0. Find the method of moments estimators of θ and λ.
Suppose Y1,··· ,Yn is a sample from a exponential distribution with mean θ, and let Y(1),···...
Suppose Y1,··· ,Yn is a sample from a exponential distribution with mean θ, and let Y(1),··· ,Y(n) denote the order statistics of the sample. (a) Find the constant c so that cY(1) is an unbiased estimator of θ. (b) Find the sufficient statistic for θ and MVUE for θ.
Problem 3. Let Y1, Y2, and Y3 be independent, identically distributed random variables from a population...
Problem 3. Let Y1, Y2, and Y3 be independent, identically distributed random variables from a population with mean µ = 12 and variance σ 2 = 192. Let Y¯ = 1/3 (Y1 + Y2 + Y3) denote the average of these three random variables. A. What is the expected value of Y¯, i.e., E(Y¯ ) =? Is Y¯ an unbiased estimator of µ? B. What is the variance of Y¯, i.e, V ar(Y¯ ) =? C. Consider a different estimator...
1. (a) Y1,Y2,...,Yn form a random sample from a probability distribution with cumulative distribution function FY...
1. (a) Y1,Y2,...,Yn form a random sample from a probability distribution with cumulative distribution function FY (y) and probability density function fY (y). Let Y(1) = min{Y1,Y2,...,Yn}. Write the cumulative distribution function for Y(1) in terms of FY (y) and hence show that the probability density function for Y(1) is fY(1)(y) = n{1−FY (y)}n−1fY (y). [8 marks] (b) An engineering system consists of 5 components connected in series, so, if one components fails, the system fails. The lifetimes (measured in...
Let Y1, ..., Yn be IID Poisson(λ) random variables. Argue that Y¯ , the sample mean,...
Let Y1, ..., Yn be IID Poisson(λ) random variables. Argue that Y¯ , the sample mean, is a sufficient statistic for λ by using the factorization criterion. Assuming that Y¯ is a complete sufficient statistic, explain why Y¯ is the minimum variance unbiased estimator.
Let Y1,Y2.....,Yn be independent ,uniformly distributed random variables on the interval[0,θ].,Y(n)=max(Y1,Y2,....,Yn),which is considered as an estimator...
Let Y1,Y2.....,Yn be independent ,uniformly distributed random variables on the interval[0,θ].,Y(n)=max(Y1,Y2,....,Yn),which is considered as an estimator of θ. Explain why Y is a good estimator for θ when sample size is large.