PLR.BIC

<code>PLR.BIC</code> takes as input a matrix of estimated parameter vectors, where each row corresponds to a covariate and each column corresponds to a value of lambda,
and returns the index of the optimal column by optimizing an information criterion. By default the BIC is used.

internal

Inference for the Lorenz and penalized Lorenz regressions. More broadly, the package proposes functions to assess inequality and graphically represent it. The Lorenz Regression procedure is introduced in Heuchenne and Jacquemain (2022) <doi:10.1016/j.csda.2021.107347> and in Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024) <doi:10.1214/23-EJS2200>.

Alexandre Jacquemain

LorenzRegression

Lorenz and Penalized Lorenz Regressions

Xingjie Shi

PLR.BIC function

<dl><dt>y</dt>
<dd>a vector of responses</dd>
<dt>x</dt>
<dd>a matrix of explanatory variables</dd>
<dt>theta</dt>
<dd>matrix gathering the path of estimated parameter vectors. Each row corresponds to a given covariate. Each column corresponds to a given value of lambda</dd>
<dt>weights</dt>
<dd>vector of sample weights. By default, each observation is given the same weight.</dd>
<dt>IC</dt>
<dd>indicates which information criterion is used. Possibles values are "BIC" (default) or "AIC".</dd></dl>

Arguments

Determines the regularization parameter (lambda) in a PLR via optimization of an information criterion. — PLR.BIC

<dl>

<dt>y</dt>
<dd>a vector of responses</dd>


<dt>x</dt>
<dd>a matrix of explanatory variables</dd>


<dt>theta</dt>
<dd>matrix gathering the path of estimated parameter vectors. Each row corresponds to a given covariate. Each column corresponds to a given value of lambda</dd>


<dt>weights</dt>
<dd>vector of sample weights. By default, each observation is given the same weight.</dd>


<dt>IC</dt>
<dd>indicates which information criterion is used. Possibles values are "BIC" (default) or "AIC".</dd>

</dl>

PLR.BIC: Determines the regularization parameter (lambda) in a PLR via optimization of an information criterion.

Description

Usage

Value

Arguments

References

See Also