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
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Author:Amos Gilat
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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
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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