compute_g

regression

classification

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

compute_g function

<dl> <dt>z</dt>
<dd>vector nonnegative values from <code>dfun</code>, e.g., u^2/2</dd> <dt>cfun</dt>
<dd>integer from 1-8, concave function as in <code>irglm_fit</code></dd> <dt>s</dt>
<dd>a numeric value, see details in <code>irglmreg_fit</code></dd> <dt>delta</dt>
<dd>a positive small value, see details in <code>irglmreg_fit</code></dd></dl>

Arguments

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

Author

Compute concave function values — compute_g

<dl>

 <dt>z</dt>
<dd>vector nonnegative values from <code>dfun</code>, e.g., u^2/2</dd>

 <dt>cfun</dt>
<dd>integer from 1-8, concave function as in <code>irglm_fit</code></dd>

 <dt>s</dt>
<dd>a numeric value, see details in <code>irglmreg_fit</code></dd>

 <dt>delta</dt>
<dd>a positive small value, see details in <code>irglmreg_fit</code></dd>

</dl>

compute_g: Compute concave function values

Description

Usage

Value

Arguments

Author

References

See Also

Examples