Question

7. Consider a random sample X1,..., Xn from a population with a Bernoulli(@) distri- bution. (a) Suppose n > 3, show that the

0 0
Add a comment Improve this question Transcribed image text
Answer #1

solution: taken from of size has been sample Random that at is given Begnollico) Y EYLO -oj **-0,1 P(4:-*;) - of herwise ALSOWe can peore 125s part using factorization the open as following if jant pmf can be written According to Factorization theoreElhiti] 50 ale (Ritu (1-0)**] -0 E [8117 CZ 30 Але flt) = ht) <*. e for sunation to be ZERO We must have above is POLY NOMYOL

Add a comment
Know the answer?
Add Answer to:
7. Consider a random sample X1,..., Xn from a population with a Bernoulli(@) distri- bution. (a)...
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. Our experts will answer your question WITHIN MINUTES for Free.
Similar Homework Help Questions
  • 2. Let X1, X2, ..., Xn be a random sample from a Bernoulli(6) distribution with prob- ability fun...

    Advanced Statistics, I need help with (c) and (d) 2. Let X1, X2, ..., Xn be a random sample from a Bernoulli(6) distribution with prob- ability function Note that, for a random variable X with a Bernoulli(8) distribution, E [X] var [X] = θ(1-0) θ and (a) Obtain the log-likelihood function, L(0), and hence show that the maximum likelihood estimator of θ is 7l i= I (b) Show that dE (0) (c) Calculate the expected information T(e) EI()] (d) Show...

  • 6-7. Let θ > 1 and let X1,X2, ,Xn be a random sample from the distri- bution with probability den...

    Will thumbs up if done neatly and correctly! 6-7. Let θ > 1 and let X1,X2, ,Xn be a random sample from the distri- bution with probability density function f(x; θ-zind, 1 < x < θ. 6. a) Obtain the maximum likelihood estimator of θ, θ b) Is a consistent estimator of θ? Justify your answer 6-7. Let θ > 1 and let X1,X2, ,Xn be a random sample from the distri- bution with probability density function f(x; θ-zind, 1

  • 7. Let X1,....Xn random sample from a Bernoulli distribution with parameter p. A random variable X...

    7. Let X1,....Xn random sample from a Bernoulli distribution with parameter p. A random variable X with Bernoulli distribution has a probability mass function (pmf) of with E(X) = p and Var(X) = p(1-p). (a) Find the method of moments (MOM) estimator of p. (b) Find a sufficient statistic for p. (Hint: Be careful when you write the joint pmf. Don't forget to sum the whole power of each term, that is, for the second term you will have (1...

  • Hint of HW2: 4. Let X = (X1, ..., Xn) be an i.i.d. sample from the...

    Hint of HW2: 4. Let X = (X1, ..., Xn) be an i.i.d. sample from the shifted exponential distri- bution with density fa,1(x) = de-1(z–a) 1(x > a), 0 := (a, 1) E O = R (0,00). Use Neyman-Fisher's theorem to: (a) show that S = X(1) is an SS for the family {fa,1}QER; (b) find an SS for the family {f1,1}x>0; (c) find an SS for the family {fa,x}o=(0,1)€0. (d) In part (a), use the procedure from Rao- Blackwell's...

  • 1. Let X1, X2,... .Xn be a random sample of size n from a Bernoulli distribution...

    1. Let X1, X2,... .Xn be a random sample of size n from a Bernoulli distribution for which p is the probability of success. We know the maximum likelihood estimator for p is p = 1 Σ_i Xi. ·Show that p is an unbiased estimator of p.

  • Problem 5 Let Xi, X2, ..., Xn be a random sample from Bernoulli(p), 0 < p...

    Problem 5 Let Xi, X2, ..., Xn be a random sample from Bernoulli(p), 0 < p < 1, and 7.i. Prove that the sample proportion is an unbiased estimator of p, i.e. p,- is an unbiased estimator of p 7.ii. Derive an expression for the variance of p,n 7.iii. Prove that the sample proportion is a consistent estimator of p. 7.iv. Prove that pn(1- Pn)

  • Let {x1, x2, ..., xn} be a sample from Bernoulli(p). Find an unbiased estimator for p^2...

    Let {x1, x2, ..., xn} be a sample from Bernoulli(p). Find an unbiased estimator for p^2 . Let {x1,x2,..., Xn} be a ..., Xn} be a sample from Bernoulli(p). Find an unbiased estimator for p?.

  • Q2 Suppose X1, X2, ..., Xn are i.i.d. Bernoulli random variables with probability of success p....

    Q2 Suppose X1, X2, ..., Xn are i.i.d. Bernoulli random variables with probability of success p. It is known that p = ΣΧ; is an unbiased estimator for p. n 1. Find E(@2) and show that p2 is a biased estimator for p. (Hint: make use of the distribution of X, and the fact that Var(Y) = E(Y2) – E(Y)2) 2. Suggest an unbiased estimator for p2. (Hint: use the fact that the sample variance is unbiased for variance.) Xi+2...

  • Let X1, . . . , Xn be a random sample from a population X with...

    Let X1, . . . , Xn be a random sample from a population X with p.d.f fθ(x) =    θ xθ−1 , for 0 < x < 1 0, otherwise, where θ > 1 is parameter. Find the MLE of 1/θ. If it is an unbiased estimator of 1/θ, compare its variance with the Cramer-Rao lower bound.

  • Problem 2: Let (X1,... Xn) denote a random variable from X having density fx(x) = 1/...

    Problem 2: Let (X1,... Xn) denote a random variable from X having density fx(x) = 1/ β,0 < x < β where β > 0 is an unknown param eter. Explain why the Cramer Rao Theorem cannot be applied to show that an unbiased estimator of β is MVU. (Hint: see slides. Condition (A) of Cramer Rao Theorem)

ADVERTISEMENT
Free Homework Help App
Download From Google Play
Scan Your Homework
to Get Instant Free Answers
Need Online Homework Help?
Ask a Question
Get Answers For Free
Most questions answered within 3 hours.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT