Description Usage Arguments Value
Composite likelihood estimators are consistent and asymptotically normal
under mild regularity conditions. The asymptotic covariance matrix is the
inverse Godambe information matrix G^{-1}, which is estimated by a
nonparametric bootstrap as the empirical covariance of B
replicates.
The sensitivity matrix H is calculated from the Hessian matrix at the
maximum composite likelihood estimates. Lastly, the variability matrix J is
obtained from the relation G=HJ^{-1}H
1 | compositemat(dat, fitted, B, use.start = FALSE, ...)
|
dat |
data matrix |
fitted |
output from the call to |
B |
number of bootstrap replicates |
use.start |
logical indicating whether to use MCLE as starting value |
... |
fixed pararameters to pass to |
a list with matrices godambe
, sensitivity
and variability
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