LPRelevance-package: Relevance-Integrated Statistical Inference Engine

In LPRelevance: Relevance-Integrated Statistical Inference Engine

Relevance-Integrated Statistical Inference Engine

Description

How to individualize a global inference method? The goal of this package is to provide a systematic recipe for converting classical global inference algorithms into customized ones. It provides methods that perform individual level inferences by taking contextual covariates into account. At the heart of our solution is the concept of "artificially-designed relevant samples", called LASERs–which pave the way to construct an inference mechanism that is simultaneously efficiently estimable and contextually relevant, thus works at both macroscopic (overall simultaneous) and microscopic (individual-level) scale.

Author(s)

Subhadeep Mukhopadhyay, Kaijun Wang

Maintainer: Kaijun Wang <[e-mail]>

References

Mukhopadhyay, S., and Wang, K (2021) "On The Problem of Relevance in Statistical Inference". <arXiv:2004.09588>

LPRelevance documentation built on May 18, 2022, 9:05 a.m.