hessianReg

Constructing Hessian matrix for regularized regression parameters.

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

hessianReg function

<dl> <dt>x</dt>
<dd>a fitted model object.</dd> <dt>which</dt>
<dd>which penalty parameter(s)?</dd> <dt>...</dt>
<dd>arguments passed to the <code>meatReg</code> function.</dd></dl>

Arguments

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

Author

Hessian Matrix of Regularized Estimators — hessianReg

<dl>

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

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

 <dt>...</dt>
<dd>arguments passed to the <code>meatReg</code> function.</dd>

</dl>

hessianReg: Hessian Matrix of Regularized Estimators

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples