Question

Suppose Y_1, Y_2,… Y_n denote a random sample of a geometric distribution with parameter p. Find...

Suppose Y_1, Y_2,… Y_n denote a random sample of a geometric distribution with parameter p. Find the maximum likelihood estimator for p.

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 Y_1, … , Y_n be a random sample from a normal distribution with unknown mu...
Let Y_1, … , Y_n be a random sample from a normal distribution with unknown mu and unknown variance sigma^2. We want to test H_0 : mu=0 versus H_a : mu !=0. Find the rejection region for the likelihood ratio test with level alpha.
suppose we draw a random sample of size n from a Poisson distribution with parameter λ....
suppose we draw a random sample of size n from a Poisson distribution with parameter λ. show that the maximum likelihood estimator for λ is an efficient estimator
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 Xl, n be a random sample from a gamma distribution with parameters a = 2...
Let Xl, n be a random sample from a gamma distribution with parameters a = 2 and p = 20.      a)         Find an estimator , using the method of maximum likelihood b) Is the estimator obtained in part a) is unbiased and consistent estimator for the parameter 0? c) Using the factorization theorem, show that the estimator found in part a) is a sufficient estimator of 0.
Suppose that X|λ is an exponential random variable with parameter λ and that λ|p is geometric...
Suppose that X|λ is an exponential random variable with parameter λ and that λ|p is geometric with parameter p. Further suppose that p is uniform between zero and one. Determine the pdf for the random variable X and compute E(X).
a is a random variable which follows geometric distribution, with parameter p = 0.1. b =...
a is a random variable which follows geometric distribution, with parameter p = 0.1. b = ln(a) 1. what is the probability mass function of b. 2 Prob (b> ln(3)) = ?
Determine the CDF of Geometric Distribution with parameter p.
Determine the CDF of Geometric Distribution with parameter p.
Let X1,X2, . . . ,Xn be a random sample of size n from a geometric...
Let X1,X2, . . . ,Xn be a random sample of size n from a geometric distribution for which p is the probability of success. (a) Find the maximum likelihood estimator of p (don't use method of moment). (b) Explain intuitively why your estimate makes good sense. (c) Use the following data to give a point estimate of p: 3 34 7 4 19 2 1 19 43 2 22 4 19 11 7 1 2 21 15 16
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...
Suppose data collected suggest a Bernoulli distribution with parameter θ (a special case of the binomial...
Suppose data collected suggest a Bernoulli distribution with parameter θ (a special case of the binomial distribution). a) Use the method of moments to obtain an estimator of θ. b) Obtain the maximum likelihood estimator (MLE) of θ.
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT