Consider independent observations y₁..... yn from the model Y,~ Poisson(μ). Using likelihood L(u) and log-likelihood /(u) as appropriate, compute the following items. 1. Derive the maximum likelihood estimate . 2. Write the second derivative of log-likelihood /(μ). 3. Give an expression of the approximated asymptotic standard error of û by plugging in the estimate 4. To this end, estimate the Fisher Information Matrix by V (μ) and then s. e. (A) = √V 4. Consider data 23, 14, 16, 22, 18, 22, 24, 30. Using your formulæ, compute and write numerical estimates 4, s. e. () and give a 95% confidence interval for u using the normal approximation.
Consider independent observations y₁..... yn from the model Y,~ Poisson(μ). Using likelihood L(u) and log-likelihood /(u) as appropriate, compute the following items. 1. Derive the maximum likelihood estimate . 2. Write the second derivative of log-likelihood /(μ). 3. Give an expression of the approximated asymptotic standard error of û by plugging in the estimate 4. To this end, estimate the Fisher Information Matrix by V (μ) and then s. e. (A) = √V 4. Consider data 23, 14, 16, 22, 18, 22, 24, 30. Using your formulæ, compute and write numerical estimates 4, s. e. () and give a 95% confidence interval for u using the normal approximation.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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![Consider independent observations y₁, ..., yn from the model Y; ~ Poisson(u). Using likelihood L(u) and log-likelihood 1(μ) as appropriate, compute the
following items.
1. Derive the maximum likelihood estimate .
2. Write the second derivative of log-likelihood 1(u).
3. Give an expression of the approximated asymptotic standard error of û by plugging in the estimate . To this end, estimate the Fisher Information Matrix
by ý = − 3 -/-1(μ) |µ=û and then s. e. (μ)
(û) =
4. Consider data 23, 14, 16, 22, 18, 22, 24, 30. Using your formulæ, compute and write numerical estimates û, s. e. (û) and give a 95% confidence interval for
μ using the normal approximation.
= √√₁²¹
VEI](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fcc2577b0-830e-4d30-98f9-831835eac3a8%2F08a0b17b-2378-4d0f-8041-e853501be75a%2Fhqm4c5g_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Consider independent observations y₁, ..., yn from the model Y; ~ Poisson(u). Using likelihood L(u) and log-likelihood 1(μ) as appropriate, compute the
following items.
1. Derive the maximum likelihood estimate .
2. Write the second derivative of log-likelihood 1(u).
3. Give an expression of the approximated asymptotic standard error of û by plugging in the estimate . To this end, estimate the Fisher Information Matrix
by ý = − 3 -/-1(μ) |µ=û and then s. e. (μ)
(û) =
4. Consider data 23, 14, 16, 22, 18, 22, 24, 30. Using your formulæ, compute and write numerical estimates û, s. e. (û) and give a 95% confidence interval for
μ using the normal approximation.
= √√₁²¹
VEI
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