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An expectation maximization algorithm for high-dimensional model selection for the Ising model with misclassified states.
| Content Provider | Europe PMC |
|---|---|
| Author | Sinclair, David G. Hooker, Giles |
| Copyright Year | 2021 |
| Abstract | We propose the misclassified Ising Model: a framework for analyzing dependent binary data where the binary state is susceptible to error. We extend previous theoretical results of a model selection method based on applying the LASSO to logistic regression at each node and show that the method will still correctly identify edges in the underlying graphical model under suitable misclassification settings. With knowledge of the misclassification process, an expectation maximization algorithm is developed that accounts for misclassification during model selection. We illustrate the increase of performance of the proposed expectation maximization algorithm with simulated data, and using data from a functional magnetic resonance imaging analysis. |
| Related Links | https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9639482&blobtype=pdf |
| Page Count | 20 |
| ISSN | 02664763 |
| Volume Number | 49 |
| DOI | 10.1080/02664763.2021.1970121 |
| PubMed Central reference number | PMC9639482 |
| Issue Number | 16 |
| PubMed reference number | 36353302 |
| Journal | Journal of Applied Statistics [J Appl Stat] |
| e-ISSN | 13600532 |
| Language | English |
| Publisher | Taylor & Francis |
| Publisher Date | 2021-08-31 |
| Access Restriction | Open |
| Rights License | © 2021 Informa UK Limited, trading as Taylor & Francis Group |
| Subject Keyword | Graphical models LASSO variational methods latent variables fMRI |
| Content Type | Text |
| Resource Type | Article |
| Subject | Statistics and Probability Statistics, Probability and Uncertainty |
