-
formulation suited to the use of log-
likelihoods (see Wilks' theorem), the test
statistic is
twice the
difference in log-
likelihoods and the
probability distribution...
-
ratio of
their likelihoods. If the more
constrained model (i.e., the null hypothesis) is
supported by the
observed data, the two
likelihoods should not differ...
- In statistics,
maximum likelihood estimation (MLE) is a
method of
estimating the
parameters of an ****umed
probability distribution,
given some observed...
-
formulation suited to the use of log-
likelihoods (see Wilks' theorem), the test
statistic is
twice the
difference in log-
likelihoods and the
probability distribution...
- p(\lambda \mid \psi )\ \operatorname {d} \!\lambda } Unfortunately,
marginal likelihoods are
generally difficult to compute.
Exact solutions are
known for a small...
- quasi-
likelihood methods are used to
estimate parameters in a
statistical model when
exact likelihood methods, for
example maximum likelihood estimation...
-
support the
likelihood principle then
inference about θ {\displaystyle \ \theta \ }
should be the same for both
cases because the two
likelihoods are proportional...
-
statistics a quasi-maximum
likelihood estimate (QMLE), also
known as a pseudo-
likelihood estimate or a
composite likelihood estimate, is an
estimate of...
-
maximum likelihood (REML)
approach is a
particular form of
maximum likelihood estimation that does not base
estimates on a
maximum likelihood fit of all...
-
partial likelihoods of each
necessary node on the way (4
partial likelihoods per node).
Having finished at the root of the tree, the
likelihood of the...
