LICbeta

This function estimates the coefficients of a linear regression model using a design matrix `X` and a response vector `Y`. It implements an A-optimal and D-optimal design criteria to choose optimal subsets of observations.

An algorithm of optimal subset selection, related to Covariance matrices, observation matrices and Response vectors (COR) to select the optimal subsets in distributed estimation. The philosophy of the package is described in Guo G. (2024) <doi:10.1007/s11222-024-10471-z>.

Guangbao Guo

The COR for Optimal Subset Selection in Distributed Estimation

Haoyue Song

Lixing Zhu

LICbeta function

<dl><dt>X</dt>
<dd>The observation matrix (n x p)</dd>
<dt>Y</dt>
<dd>The response vector (n x 1)</dd>
<dt>alpha</dt>
<dd>The significance level for computing confidence intervals</dd>
<dt>K</dt>
<dd>The number of subsets</dd>
<dt>nk</dt>
<dd>The number of observations per subset</dd></dl>

Arguments

Calculate the LIC estimator for linear regression — LICbeta

<dl>

<dt>X</dt>
<dd>The observation matrix (n x p)</dd>


<dt>Y</dt>
<dd>The response vector (n x 1)</dd>


<dt>alpha</dt>
<dd>The significance level for computing confidence intervals</dd>


<dt>K</dt>
<dd>The number of subsets</dd>


<dt>nk</dt>
<dd>The number of observations per subset</dd>

</dl>

LICbeta: Calculate the LIC estimator for linear regression

Description

Usage

Value

Arguments

References