se.zipath

Generic function for computing standard errors of non-zero regularized estimators

regression

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

Achim Zeileis

Simon Jackman

Brian Ripley

Patrick Breheny

se function

<dl> <dt>x</dt>
<dd>a fitted model object.</dd> <dt>which</dt>
<dd>which penalty parameter(s)?</dd> <dt>log</dt>
<dd>if TRUE, the computed standard error is for log(theta) for negative binomial regression, otherwise, for theta.</dd> <dt>...</dt>
<dd>arguments passed to methods.</dd></dl>

Arguments

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

Author

Standard Error of Regularized Estimators — se

<dl>

 <dt>x</dt>
<dd>a fitted model object.</dd>

 <dt>which</dt>
<dd>which penalty parameter(s)?</dd>

 <dt>log</dt>
<dd>if TRUE, the computed standard error is for log(theta) for negative binomial regression, otherwise, for theta.</dd>

 <dt>...</dt>
<dd>arguments passed to methods.</dd>

</dl>

se: Standard Error of Regularized Estimators

Description

Usage

Value

Arguments

Author

References

See Also

Examples