Problem 2. Poisson regression. You observe nonnegative integer-valued data
Y_(1),dots,Y_(n)
and an
n\times k
data matrix
x
where the
i
th row,
x_(i)^(TT)
, is a
k
-dimensional vector of covariates for individual
i
. Assume
Y_(i)?Pois(\lambda _(i))
independently and
\lambda _(i)=exp(x_(i)^(TT)\beta )
, with
\beta
an unknown
k
-vector of regression coefficients. If you don't like working with vectors or matrices, you may choose to solve this problem for
k=1
, in which case
\lambda _(i)=exp(x_(i)\beta )
with
x_(i)
a scalar covariate and
\beta
a scalar parameter. (a) Write down the likelihood function for
\beta
. Identify a minimal sufficient statistic. (b) Show that the likelihood has a unique maximum, and provide the equation that defines the MLE
hat(\beta )
. (c) Specify the approximate distribution of the MLE for
n
large.