sandwich

The <code>sandwich</code> function provides an estimate for the asymptotic variance
and covariance matrix of the parameter estimators of the power logit (or log-log)
regression models based on de sandwich estimator (see Queiroz and Ferrari (2022)).

Power logit regression models for bounded
continuous data, in which the density generator may be normal, Student-t,
power exponential, slash, hyperbolic, sinh-normal, or type II logistic.
Diagnostic tools associated with the fitted model, such as the residuals,
local influence measures, leverage measures, and goodness-of-fit statistics,
are implemented. The estimation process follows the maximum likelihood approach
and, currently, the package supports two types of estimators: the usual maximum
likelihood estimator and the penalized maximum likelihood estimator. More details
about power logit regression models are described in
Queiroz and Ferrari (2022) <arXiv:2202.01697>.

Felipe Queiroz

PLreg

Power Logit Regression for Modeling Bounded Data

Silvia Ferrari

sandwich function

<dl><dt>object</dt>
<dd>fitted model object of class "<code>PLreg</code>".</dd></dl>

Arguments

Sandwich Variance and Covariance Matrix for PLreg Objects — sandwich

<dl>

<dt>object</dt>
<dd>fitted model object of class "<code>PLreg</code>".</dd>

</dl>

sandwich: Sandwich Variance and Covariance Matrix for PLreg Objects

Description

Usage

Value

Arguments

References

See Also

Examples