Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection
| العنوان: | Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection |
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| المؤلفون: | Irini Moustaki, Giampiero Marra, Elena Geminiani |
| المصدر: | Psychometrika |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Mathematical optimization, Theory and Methods, Psychometrics, Computer science, Degrees of freedom (statistics), Trust, 01 natural sciences, 010104 statistics & probability, Matrix (mathematics), 0504 sociology, HA Statistics, Computer Simulation, QA Mathematics, simple structure, Differentiable function, 0101 mathematics, penalized likelihood, General Psychology, Selection (genetic algorithm), Factor analysis, Likelihood Functions, Trust region, effective degrees of freedom, Applied Mathematics, Model selection, 05 social sciences, Process (computing), 050401 social sciences methods, measurement invariance, generalized information criterion, Algorithms |
| الوصف: | Penalized factor analysis is an efficient technique that produces a factor loading matrix with many zero elements thanks to the introduction of sparsity-inducing penalties within the estimation process. However, sparse solutions and stable model selection procedures are only possible if the employed penalty is non-differentiable, which poses certain theoretical and computational challenges. This article proposes a general penalized likelihood-based estimation approach for single- and multiple-group factor analysis models. The framework builds upon differentiable approximations of non-differentiable penalties, a theoretically founded definition of degrees of freedom, and an algorithm with integrated automatic multiple tuning parameter selection that exploits second-order analytical derivative information. The proposed approach is evaluated in two simulation studies and illustrated using a real data set. All the necessary routines are integrated into the R package penfa. Supplementary Information The online version contains supplementary material available at 10.1007/s11336-021-09751-8. |
| وصف الملف: | text |
| تدمد: | 1860-0980 0033-3123 |
| DOI: | 10.1007/s11336-021-09751-8 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7693055f4bea0a18b5d1f2f6eae7244 https://doi.org/10.1007/s11336-021-09751-8 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b7693055f4bea0a18b5d1f2f6eae7244 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 18600980 00333123 |
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| DOI: | 10.1007/s11336-021-09751-8 |