Forthcoming

Identifiability Analysis Using Data Cloning

Accepted: March 2022

Authors

Keywords:

identifiability, data cloning, dynamic models, MCMC algorithms

Abstract

Lack of identifiability in statistical models may hinder unique inferential conclusions. Therefore, the search for parametric constraints that ensure identifiability is of utmost importance in statistics. However, for complex modeling strategies, even acquiring the knowledge that the model is unidentifiable may prove very difficult. In this paper, we investigate the use of Data Cloning, a modern algorithm for classical inference in latent variable models, as a tool for assessing model identifiability. We discuss its advantages and disadvantages and illustrate its use with a dynamic linear model.

Published

2022-03-30

How to Cite

Sartori Junior , J. A., & D’Elia Branco, M. (2022). Identifiability Analysis Using Data Cloning: Accepted: March 2022. REVSTAT-Statistical Journal. Retrieved from https://revstat.ine.pt/index.php/REVSTAT/article/view/457

Issue

Section

Forthcoming Paper