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7. Let X1,....Xn random sample from a Bernoulli distribution with parameter p. A random variable X...
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.
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...
Let X,, X,,...X be a random sample of size n from a normal distribution with parameters a. Derive the Cramer-Rao lower...
Let X,, X,,...X be a random sample of size n from a normal distribution with parameters a. Derive the Cramer-Rao lower bound matrix for an unbiased estimator of the vector of parameters (μ, σ2). b. Using the Cramer-Rao lower bound prove that the sample mean X is the minimum variance unbiased estimator of u Is the maximum likelihood estimator of σ--σ-->|··( X,-X ) unbiased? c.
Let X,, X,,...X be a random sample of size n from a normal distribution with...
8.60-Modified: Let X1,...,Xn be i.i.d. from an exponential distribution with the density function...
8.60-Modified: Let X1,...,Xn be i.i.d. from an exponential distribution with the density function a. Check the assumptions, and find the Fisher information I(T) b. Find CRLB c. Find sufficient statistic for τ. d. Show that t = X1 is unbiased, and use Rao-Blackwellization to construct MVUE for τ. e. Find the MLE of r. f. What is the exact sampling distribution of the MLE? g. Use the central limit theorem to find a normal approximation to the sampling distribution h....
Let Sı, S2,... , Sn be a random sample from a Bernoulli distribution with pmf (a) Calculate E(S) and find the MOM estim...
Let Sı, S2,... , Sn be a random sample from a Bernoulli distribution with pmf (a) Calculate E(S) and find the MOM estimator of p. (b) Construct the log-likelihood function and use this to find the max- imum likeligood (ML) estimator of p.
Let Sı, S2,... , Sn be a random sample from a Bernoulli distribution with pmf (a) Calculate E(S) and find the MOM estimator of p. (b) Construct the log-likelihood function and use this to find the max-...
3. Consider a discrete random variable X which follows the geometric distribution f(x,p) = pr-1(1...
3. Consider a discrete random variable X which follows the geometric distribution f(x,p) = pr-1(1-p), x = 1.2. . . . , 0 < p < 1. Recall that E(x) (1-p) (a) Find the Fisher information I(p). (b) Show that the Cramer-Rao inequality is strict e) Let XX ~X. Find the maximum likelihood estimator of p. Note that the expression you find may look complicated and hard to evaluate. (d) Now modify your view by setting μ T1p such that...
2 Let X1, X2, ..., X, be independent continuous random variables from the following distribution: f(x)...
2 Let X1, X2, ..., X, be independent continuous random variables from the following distribution: f(x) = or-(-) where x 2 1 and a > 1 You may use the fact: E[X] - - 2.4 Show that the fisher information in the whole sample is: In(a)= 2.5 What Cramer Rao lower bound for unbiased estimators of a? 2.7 Consider estimating the unknown quantity: g(a) = 0 - 4+.. Determine the MLE of gla). What property are you using to justify...
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.
7. Consider a random sample X1,..., Xn from a population with a Bernoulli(@) distri- bution. (a)...
7. Consider a random sample X1,..., Xn from a population with a Bernoulli(@) distri- bution. (a) Suppose n > 3, show that the product W = X X X3 is an unbiased estimator of p. (b) Show that T = 2h-1X; is a sufficient statistic for 0 (c) Using your answers to parts (a) and (b), use the Rao-Blackwell Theorem to find a better unbiased estimator of 03. (Make sure you account for all cases) (d) Show that T =...
2. (Discrete uniform). Consider the PMF P(X x)= for x 1,2,...0 _ You have a random...
2. (Discrete uniform). Consider the PMF P(X x)= for x 1,2,...0 _ You have a random sample of size three from this distribution: {2,3,10}. a. Find the method of moments estimate for 0 HINT: a very useful fact is that k1 n(n+1) 2 b. Find the MLE for 0 c. Which estimator is unbiased? d. Which estimator is preferred?
2. (Discrete uniform). Consider the PMF P(X x)= for x 1,2,...0 _ You have a random sample of size three from...
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...
Let X,, X,,...X be a random sample of size n from a normal distribution with parameters a. Derive the Cramer-Rao lower bound matrix for an unbiased estimator of the vector of parameters (μ, σ2). b. Using the Cramer-Rao lower bound prove that the sample mean X is the minimum variance unbiased estimator of u Is the maximum likelihood estimator of σ--σ-->|··( X,-X ) unbiased? c.
Let X,, X,,...X be a random sample of size n from a normal distribution with...
8.60-Modified: Let X1,...,Xn be i.i.d. from an exponential distribution with the density function a. Check the assumptions, and find the Fisher information I(T) b. Find CRLB c. Find sufficient statistic for τ. d. Show that t = X1 is unbiased, and use Rao-Blackwellization to construct MVUE for τ. e. Find the MLE of r. f. What is the exact sampling distribution of the MLE? g. Use the central limit theorem to find a normal approximation to the sampling distribution h....
Let Sı, S2,... , Sn be a random sample from a Bernoulli distribution with pmf (a) Calculate E(S) and find the MOM estimator of p. (b) Construct the log-likelihood function and use this to find the max- imum likeligood (ML) estimator of p.
Let Sı, S2,... , Sn be a random sample from a Bernoulli distribution with pmf (a) Calculate E(S) and find the MOM estimator of p. (b) Construct the log-likelihood function and use this to find the max-...
3. Consider a discrete random variable X which follows the geometric distribution f(x,p) = pr-1(1-p), x = 1.2. . . . , 0 < p < 1. Recall that E(x) (1-p) (a) Find the Fisher information I(p). (b) Show that the Cramer-Rao inequality is strict e) Let XX ~X. Find the maximum likelihood estimator of p. Note that the expression you find may look complicated and hard to evaluate. (d) Now modify your view by setting μ T1p such that...
2 Let X1, X2, ..., X, be independent continuous random variables from the following distribution: f(x) = or-(-) where x 2 1 and a > 1 You may use the fact: E[X] - - 2.4 Show that the fisher information in the whole sample is: In(a)= 2.5 What Cramer Rao lower bound for unbiased estimators of a? 2.7 Consider estimating the unknown quantity: g(a) = 0 - 4+.. Determine the MLE of gla). What property are you using to justify...
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.
7. Consider a random sample X1,..., Xn from a population with a Bernoulli(@) distri- bution. (a) Suppose n > 3, show that the product W = X X X3 is an unbiased estimator of p. (b) Show that T = 2h-1X; is a sufficient statistic for 0 (c) Using your answers to parts (a) and (b), use the Rao-Blackwell Theorem to find a better unbiased estimator of 03. (Make sure you account for all cases) (d) Show that T =...
2. (Discrete uniform). Consider the PMF P(X x)= for x 1,2,...0 _ You have a random sample of size three from this distribution: {2,3,10}. a. Find the method of moments estimate for 0 HINT: a very useful fact is that k1 n(n+1) 2 b. Find the MLE for 0 c. Which estimator is unbiased? d. Which estimator is preferred?
2. (Discrete uniform). Consider the PMF P(X x)= for x 1,2,...0 _ You have a random sample of size three from...
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