Identifiability Analysis Using Data Cloning
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 unide...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Instituto Nacional de Estatística | Statistics Portugal
2024-02-01
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| Series: | Revstat Statistical Journal |
| Subjects: | |
| Online Access: | https://revstat.ine.pt/index.php/REVSTAT/article/view/457 |
| Summary: | 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.
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| ISSN: | 1645-6726 2183-0371 |