estfunReg

estfunReg.zipath

Generic function for extracting the empirical first derivative of log-likelihood function of a fitted regularized model.

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

estfunReg function

<dl> <dt>x</dt>
<dd>a fitted model object.</dd> <dt>...</dt>
<dd>arguments passed to methods.</dd></dl>

Arguments

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

Author

Extract Empirical First Derivative of Log-likelihood Function — estfunReg

<dl>

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

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

</dl>

estfunReg: Extract Empirical First Derivative of Log-likelihood Function

Description

Usage

Value

Arguments

Author

References

See Also

Examples