{"paper":{"title":"Nonparametric estimation of mixing densities for discrete distributions","license":"","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Fran\\c{c}ois Roueff, Tobias Ryd\\'en","submitted_at":"2006-02-10T14:42:21Z","abstract_excerpt":"By a mixture density is meant a density of the form $\\pi_{\\mu}(\\cdot)=\\int\\pi_{\\theta}(\\cdot)\\times\\mu(d\\theta)$, where $(\\pi_{\\theta})_{\\theta\\in\\Theta}$ is a family of probability densities and $\\mu$ is a probability measure on $\\Theta$. We consider the problem of identifying the unknown part of this model, the mixing distribution $\\mu$, from a finite sample of independent observations from $\\pi_{\\mu}$. Assuming that the mixing distribution has a density function, we wish to estimate this density within appropriate function classes. A general approach is proposed and its scope of application"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"math/0602217","kind":"arxiv","version":1},"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"}