pqlBpoisson2

The average weighted estimator and the unknown weighted estimator of the PQL in Poisson-GLMS through damped Gauss-Newton

In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.

Guangbao Guo

A Partitioned Quasi-Likelihood for Distributed Statistical
Inference

pqlBpoisson2 function

<dl><dt>data</dt>
<dd>is a design matrix with uniform distribution and the response vector</dd><dt>G</dt>
<dd>is the number of subsets.</dd><dt>nk</dt>
<dd>is the size of subsets.</dd></dl>

Arguments

The weighted Gauss-Newton estimators of the PQL in Poisson-GLMS — pqlBpoisson2

<dl>

<dt>data</dt>
<dd>is a design matrix with uniform distribution and the response vector</dd>

<dt>G</dt>
<dd>is the number of subsets.</dd>

<dt>nk</dt>
<dd>is the size of subsets.</dd>

</dl>

pqlBpoisson2: The weighted Gauss-Newton estimators of the PQL in Poisson-GLMS

Description

Usage

Value

Arguments

Examples