summary.glmregNB

print.summary.glmregNB

Summary results of fitted penalized negative binomial regression model

models

Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>,
Wang et al. (2015) <doi:10.1002/bimj.201400143>,
Wang et al. (2016) <doi:10.1177/0962280214530608>,
Wang (2021) <doi:10.1007/s11749-021-00770-2>,
Wang (2024) <doi:10.1111/anzs.12409>.

Zhu Wang

mpath

Regularized Linear Models

summary.glmregNB function

<dl><dt>object</dt>
<dd>fitted model object of class <code>glmregNB</code>.</dd><dt>...</dt>
<dd>arguments passed to or from other methods.</dd>
</dl>

Arguments

Zhu Wang &lt;zwang145@uthsc.edu&gt;

Author

Summary Method Function for Objects of Class 'glmregNB' — summary.glmregNB

<dl>

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

<dt>...</dt>
<dd>arguments passed to or from other methods.</dd>
</dl>

summary.glmregNB: Summary Method Function for Objects of Class 'glmregNB'

Description

Usage

Value

Arguments

Author

Details

References

See Also