pqlLogist

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

pqlLogist function

<dl><dt>data</dt>
<dd>data is a highly correlated data set</dd><dt>G</dt>
<dd>G is the number of nodes</dd><dt>nk</dt>
<dd>n1 is the length of each data subset</dd></dl>

Arguments

pqlLogist — pqlLogist

<dl>

<dt>data</dt>
<dd>data is a highly correlated data set</dd>

<dt>G</dt>
<dd>G is the number of nodes</dd>

<dt>nk</dt>
<dd>n1 is the length of each data subset</dd>

</dl>

pqlLogist: pqlLogist

Description

Usage

Value

Arguments

Examples