Exercise 2 Let (y1,...,y5) and (36,..., 910) be two independent samples from a Poisson distribution of mean exp{1} and from a Poisson distribution of mean exp{ẞ1 + ẞ2}, respectively. a) Formulate an appropriate Poisson regression model for the expected value of Yi, i = 1,..., 10. b) Write the log-likelihood function of ẞ= (B1, B2) and the score function. Find the maximum likelihood estimate of (B1, B2). Finally, obtain the observed information matrix. c) Determine an approximate distribution of the maximum likelihood estimator of B (B1, B2), and an approximate distribution of the maximum likelihood estimator B₁ of ẞ₁. d) Provide the interpretation of the coefficient ẞ2. = e) Define the concept of "saturated model" and obtain the expression of maximum of the log-likelihood for this model.

Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015
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Chapter4: Writing Linear Equations
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Exercise 2
Let (y1,...,y5) and (36,..., 910) be two independent samples from a Poisson distribution of mean
exp{1} and from a Poisson distribution of mean exp{ẞ1 + ẞ2}, respectively.
a) Formulate an appropriate Poisson regression model for the expected value of Yi, i =
1,..., 10.
b) Write the log-likelihood function of ẞ= (B1, B2) and the score function. Find the maximum
likelihood estimate of (B1, B2). Finally, obtain the observed information matrix.
c) Determine an approximate distribution of the maximum likelihood estimator of B
(B1, B2), and an approximate distribution of the maximum likelihood estimator B₁ of ẞ₁.
d) Provide the interpretation of the coefficient ẞ2.
=
e) Define the concept of "saturated model" and obtain the expression of maximum of the
log-likelihood for this model.
Transcribed Image Text:Exercise 2 Let (y1,...,y5) and (36,..., 910) be two independent samples from a Poisson distribution of mean exp{1} and from a Poisson distribution of mean exp{ẞ1 + ẞ2}, respectively. a) Formulate an appropriate Poisson regression model for the expected value of Yi, i = 1,..., 10. b) Write the log-likelihood function of ẞ= (B1, B2) and the score function. Find the maximum likelihood estimate of (B1, B2). Finally, obtain the observed information matrix. c) Determine an approximate distribution of the maximum likelihood estimator of B (B1, B2), and an approximate distribution of the maximum likelihood estimator B₁ of ẞ₁. d) Provide the interpretation of the coefficient ẞ2. = e) Define the concept of "saturated model" and obtain the expression of maximum of the log-likelihood for this model.
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