Open AccessNon-Convex Penalized Estimation of Count Data Responses via Generalized Linear Model (GLM)
Author(s) -
Rasaki Olawale Olanrewaju,
J. F. Ojo
Publication year - 2020
Publication title -
asian journal of fuzzy and applied mathematics
Language(s) - English
Resource type - Journals
ISSN - 2321-564X
DOI - 10.24203/ajfam.v8i3.6443
Subject(s) - generalized linear model , count data , mathematics , covariate , statistics , poisson distribution , scad , sample size determination , negative binomial distribution , linear regression , binomial regression , poisson regression , quasi likelihood , linear model , minimax , regression analysis , mathematical optimization , medicine , population , environmental health , psychiatry , myocardial infarction
This study provided a non-convex penalized estimation procedure via Smoothed Clipped Absolute Deviation (SCAD) and Minimax Concave Penalty (MCP) for count data responses to checkmate the problem of covariates exceeding the sample size . The Generalized Linear Model (GLM) approach was adopted in obtaining the penalized functions needed by the MCP and SCAD non-convex penalizations of Binomial, Poisson and Negative-Binomial related count responses regression. A case study of the colorectal cancer with six (6) covariates against sample size of five (5) was subjected to the non-convex penalized estimation of the three distributions. It was revealed that the non-convex penalization of Binomial regression via MCP and SCAD best explained four un-penalized covariates needed in determining whether surgical or therapy ideal for treating the turmoil.