{"paper":{"title":"A Proximal Point Algorithm for Minimum Divergence Estimators with Application to Mixture Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Diaa Al Mohamad, Michel Broniatowski","submitted_at":"2016-03-23T10:19:16Z","abstract_excerpt":"Estimators derived from a divergence criterion such as $\\varphi-$divergences are generally more robust than the maximum likelihood ones. We are interested in particular in the so-called MD$\\varphi$DE, an estimator built using a dual representation of $\\varphi$--divergences. We present in this paper an iterative proximal point algorithm which permits to calculate such estimator. This algorithm contains by its construction the well-known EM algorithm. Our work is based on the paper of \\citep{Tseng} on the likelihood function. We provide several convergence properties of the sequence generated by"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.07117","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}