Quasi-Maximum Likelihood based Model Selection Procedures for Binary Outcomes
Year: 2021
Type: preprint
Abstract: In this paper, I propose two model selection procedures based on a quasi-maximum likelihood estimator when there exist unmeasured covariates. I prove that a proposed BIC-type model selection procedure has model selection consistency, and confirm these property through simulation datasets.
Source: arXiv (Cornell University)
Author: Shunichiro Orihara
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3171339767