This exercise set contains some solved exercises on set estimation of the mean. The theory needed to solve these exercises is introduced in the lecture entitled Set estimation of the mean.
Suppose you observe a sample
of
independent draws from a normal distribution having
unknown mean
and known variance
.
Denote the
draws by
,
...,
.
Suppose their sample mean
is equal to
,
i.e.:
Find a confidence interval for
,
using a set estimator of
having
coverage probability.
For a given sample size
,
the interval
estimator
has
coverage
probability
where
is a standard normal random variable and
is a strictly positive constant. Thus, we need to find
such
that
But
where
the last equality stems from the fact that the standard normal distribution is
symmetric around zero. Therefore
must be such
that:
or:
Using
normal distribution tables or a computer program to find the value of
(see the lecture entitled Normal
distribution - Values), we
obtain:
Thus,
the confidence interval for
is:
Suppose you observe a sample of
independent draws from a normal distribution having unknown mean
and unknown variance
.
Denote the
draws by
,
...,
.
Suppose their sample mean
is equal to
,
i.e.:
and
their adjusted sample variance
is equal to
,
i.e.:
Find a confidence interval for
,
using a set estimator of
having
coverage probability.
For a given sample size
,
the interval
estimator
has
coverage
probability
where
is a standard Student's t random variable with
degrees of freedom and
is a strictly positive constant. Thus, we need to find
such
that
But
where
the last equality stems from the fact that the standard Student's t
distribution is symmetric around zero. Therefore
must be such
that:
or:
Using
a computer program to find the value of
(for example, with the MATLAB command tinv(0.995,99)),
we
obtain:
Thus,
the confidence interval for
is: