Question

X1, X2, X3, . . . , Xn is a normal distribution with a mean µ...

X1, X2, X3, . . . , Xn is a normal distribution with a mean µ and variance σ2 which is from a random sample from a population

  1. if σ2 is known, what is the maximum likelihood estimation of μ2
  2. does the maximum likelihood estimation of μ2 found in question 1 satisfy unbiasedness? If not, what is the bias?
  3. does the maximum likelihood estimation of μ2 found in question 1 satisfy consistency?

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
Suppose X1, · · · , Xn from a normal distribution N(µ, σ2 ) where µ...
Suppose X1, · · · , Xn from a normal distribution N(µ, σ2 ) where µ is unknown but σ is known. Consider the following hypothesis testing problem: H0 : µ = µ0 vs. Ha : µ > µ0 Prove that the decision rule is that we reject H0 if X¯ − µ0 σ/√ n > Z(1 − α), where α is the significant level, and show that this is equivalent to rejecting H0 if µ0 is less than the...
Let X1, X2, X3 be a random sample from a population. The distribution of the population...
Let X1, X2, X3 be a random sample from a population. The distribution of the population has a parameter θ, and it is known that E[Xi] = 2θ and Var[Xi] = σ2 for i = 1,2,3. Consider the following two point estimators for θ: W1= (1/8)X1+ (1/4)X2+ (1/8)X3 and W2= (1/6)X1+ (1/4)X2+ (1/12)X3 Which is the better estimator for θ? Why?
Let X1, X2, . . . , Xn be a random sample from the normal distribution...
Let X1, X2, . . . , Xn be a random sample from the normal distribution N(µ, 36). (a) Show that a uniformly most powerful critical region for testing H0 : µ = 50 against H1 : µ < 50 is given by C2 = {x : x ≤ c}. Find the values of c for α = 0.10.
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
Suppose that X1, X2, . . . , Xn are independent identically distributed random variables with...
Suppose that X1, X2, . . . , Xn are independent identically distributed random variables with variance σ2. Let Y1 = X2 +X3 , Y2 = X1 +X3 and Y3 = X1 + X2. Find the following : (in terms of σ2) (a) Var(Y1) (b) cov(Y1 , Y2 ) (c) cov(X1 , Y1 ) (d) Var[(Y1 + Y2 + Y3)/2]
Let X1, X2, ·······, Xn be a random sample from the Bernoulli distribution. Under the condition...
Let X1, X2, ·······, Xn be a random sample from the Bernoulli distribution. Under the condition 1/2≤Θ≤1, find a maximum-likelihood estimator of Θ.
Let X1, X2, X3 be a random sample of size 3 from a distribution that is...
Let X1, X2, X3 be a random sample of size 3 from a distribution that is Normal with mean 9 and variance 4. (a) Determine the probability that the maximum of X1; X2; X3 exceeds 12. (b) Determine the probability that the median of X1; X2; X3 less than 10. Please I need a solution that uses the pdf/CDF of the corresponding order statistics.
Let X1, X2, X3 be a random sample of size 3 from a distribution that is...
Let X1, X2, X3 be a random sample of size 3 from a distribution that is Normal with mean 9 and variance 4. (a) Determine the probability that the maximum of X1; X2; X3 exceeds 12. (b) Determine the probability that the median of X1; X2; X3 less than 10. (c) Determine the probability that the sample mean of X1; X2; X3 less than 10. (Use R or other software to find the probability.)
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
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.