{"paper":{"title":"Non-parametric estimation of conditional densities: A new method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Dag Tj{\\o}stheim, H{\\aa}kon Otneim","submitted_at":"2016-10-17T09:58:22Z","abstract_excerpt":"Let $\\textbf{X} = (X_1,\\ldots, X_p)$ be a stochastic vector having joint density function $f_{\\textbf{X}}(x)$ with partitions $\\textbf{X}_1 = (X_1,\\ldots, X_k)$ and $\\textbf{X}_2 = (X_{k+1},\\ldots, X_p)$. A new method for estimating the conditional density function of $\\textbf{X}_1$ given $\\textbf{X}_2$ is presented. It is based on locally Gaussian approximations, but simplified in order to tackle the curse of dimensionality in multivariate applications, where both response and explanatory variables can be vectors. We compare our method to some available competitors, and the error of approxima"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.05035","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"}