Ohit: OGA+HDIC+Trim and High-Dimensional Linear Regression Models

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.

Getting started

Package details

AuthorHai-Tang Chiou, Ching-Kang Ing, Tze Leung Lai
MaintainerHai-Tang Chiou <htchiou1@gmail.com>
URL http://mx.nthu.edu.tw/~cking/pdf/IngLai2011.pdf
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the Ohit package in your browser

Any scripts or data that you put into this service are public.

Ohit documentation built on May 1, 2019, 8:43 p.m.