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...
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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 |
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| Online Access: | https://revstat.ine.pt/index.php/REVSTAT/article/view/457 |
| _version_ | 1797300142803517440 |
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| author | José Augusto Sartori Junior Márcia D’Elia Branco |
| author_facet | José Augusto Sartori Junior Márcia D’Elia Branco |
| author_sort | José Augusto Sartori Junior |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-03-07T23:01:55Z |
| format | Article |
| id | doaj.art-2c15a1cf880847b18d88c65c63cb90ac |
| institution | Directory Open Access Journal |
| issn | 1645-6726 2183-0371 |
| language | English |
| last_indexed | 2024-03-07T23:01:55Z |
| publishDate | 2024-02-01 |
| publisher | Instituto Nacional de Estatística | Statistics Portugal |
| record_format | Article |
| series | Revstat Statistical Journal |
| spelling | doaj.art-2c15a1cf880847b18d88c65c63cb90ac2024-02-22T12:22:32ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712024-02-0122110.57805/revstat.v22i1.457Identifiability Analysis Using Data CloningJosé Augusto Sartori Junior 0Márcia D’Elia Branco1University of Sao PauloUniversity of Sao Paulo 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. https://revstat.ine.pt/index.php/REVSTAT/article/view/457identifiabilitydata cloningdynamic modelsMCMC algorithms |
| spellingShingle | José Augusto Sartori Junior Márcia D’Elia Branco Identifiability Analysis Using Data Cloning Revstat Statistical Journal identifiability data cloning dynamic models MCMC algorithms |
| title | Identifiability Analysis Using Data Cloning |
| title_full | Identifiability Analysis Using Data Cloning |
| title_fullStr | Identifiability Analysis Using Data Cloning |
| title_full_unstemmed | Identifiability Analysis Using Data Cloning |
| title_short | Identifiability Analysis Using Data Cloning |
| title_sort | identifiability analysis using data cloning |
| topic | identifiability data cloning dynamic models MCMC algorithms |
| url | https://revstat.ine.pt/index.php/REVSTAT/article/view/457 |
| work_keys_str_mv | AT joseaugustosartorijunior identifiabilityanalysisusingdatacloning AT marciadeliabranco identifiabilityanalysisusingdatacloning |