{"id":51512915,"url":"https://github.com/computorg/published-202311-delattre-fim","last_synced_at":"2026-07-08T08:02:27.152Z","repository":{"id":208241313,"uuid":"719491493","full_name":"computorg/published-202311-delattre-fim","owner":"computorg","description":"Computing  an empirical  Fisher information matrix estimate in latent variable models through stochastic approximation","archived":false,"fork":false,"pushed_at":"2025-09-12T13:39:41.000Z","size":6150,"stargazers_count":0,"open_issues_count":3,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-09-12T16:04:18.436Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://computo-journal.org/published-202311-delattre-fim/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/computorg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2023-11-16T09:30:58.000Z","updated_at":"2025-09-12T13:39:45.000Z","dependencies_parsed_at":"2024-01-08T16:27:58.602Z","dependency_job_id":"5320fbd3-80fc-41ed-ab62-ecf60dde0cca","html_url":"https://github.com/computorg/published-202311-delattre-fim","commit_stats":null,"previous_names":["computorg/published-202311-delattre-fim"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/computorg/published-202311-delattre-fim","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202311-delattre-fim","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202311-delattre-fim/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202311-delattre-fim/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202311-delattre-fim/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/computorg","download_url":"https://codeload.github.com/computorg/published-202311-delattre-fim/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/computorg%2Fpublished-202311-delattre-fim/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35257142,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-08T02:00:06.796Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-07-08T08:02:26.378Z","updated_at":"2026-07-08T08:02:27.139Z","avatar_url":"https://github.com/computorg.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Computing an empirical Fisher information matrix estimate in latent variable models through stochastic approximation\nMaud Delattre, Estelle Kuhn\n2023-11-21\n\n### Citation\n\nMaud Delattre and Estelle Kuhn (November 2023). Computing an empirical Fisher information matrix estimate in latent variable models through stochastic approximation. Computo.\n\u003chttps://doi.org/10.57750/r5gx-jk62\u003e\n\n### Badges\n\n[![build and\npublish](https://github.com/computorg/published-202311-delattre-fim/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-202311-delattre-fim/actions/workflows/build.yml)\n[![reviews](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-202311-delattre-fim/issues?q=is%3Aopen+is%3Aissue+label%3Areview)\n[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-202311-delattre-fim)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-202311-delattre-fim)\n[![DOI:10.57750/r5gx-jk62](https://img.shields.io/badge/DOI-10.57750%2Fr5gx--jk62-034E79.svg)](https://doi.org/10.57750/r5gx-jk62)\n[![Creative Commons\nLicense](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)\n\n### Authors’ affiliations\n\n- Maud Delattre (Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France)\n- Estelle Kuhn (Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France)\n\n### Abstract\n\nThe Fisher information matrix (FIM) is a key quantity in statistics.\nHowever its exact computation is often not trivial. In particular in\nmany latent variable models, it is intricated due to the presence of\nunobserved variables. Several methods have been proposed to approximate\nthe FIM when it can not be evaluated analytically. Different estimates\nhave been considered, in particular moment estimates. However some of\nthem require to compute second derivatives of the complete data\nlog-likelihood which leads to some disadvantages. In this paper, we\nfocus on the empirical Fisher information matrix defined as an empirical\nestimate of the covariance matrix of the score, which only requires to\ncompute the first derivatives of the log-likelihood. Our contribution\nconsists in presenting a new numerical method to evaluate this empirical\nFisher information matrix in latent variable model when the proposed\nestimate can not be directly analytically evaluated. We propose a\nstochastic approximation estimation algorithm to compute this estimate\nas a by-product of the parameter estimate. We evaluate the finite sample\nsize properties of the proposed estimate and the convergence properties\nof the estimation algorithm through simulation studies.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202311-delattre-fim","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcomputorg%2Fpublished-202311-delattre-fim","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202311-delattre-fim/lists"}