{"paper":{"title":"Tree-aggregated regression for compositional data with measurement errors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Tree aggregation of compositional data converts independent leaf measurement errors into level-dependent correlated contamination across nodes.","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Tianying Wang, Zhenghan Li","submitted_at":"2026-05-14T23:16:34Z","abstract_excerpt":"High-dimensional compositional covariates, often derived from count data, are subject to measurement error and are frequently analyzed after aggregation along a prespecified tree to improve interpretability in applications such as microbiome studies. Existing approaches typically handle either tree-guided compositional regression or errors-in-variables correction, but they do not account for the hierarchical contamination induced by their interaction. We show that tree aggregation turns leaf-level measurement error into level-dependent, correlated contamination across aggregated nodes, which i"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We show that tree aggregation turns leaf-level measurement error into level-dependent, correlated contamination across aggregated nodes... We propose TARCO, which integrates bias-corrected estimating quantities with a tree-aware positive semidefinite stabilization and sparse regularization... We establish finite-sample bounds for prediction and estimation errors and prove sign consistency under conditions that explicitly reflect tree heterogeneity.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The tree structure is prespecified and known, and either the measurement-error covariance is known or a consistent estimator for it is available; the finite-sample bounds and sign consistency hold only under conditions that explicitly reflect tree heterogeneity.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"TARCO corrects measurement-error-induced correlated contamination in tree-aggregated compositional regression via bias-corrected estimating equations, tree-aware PSD stabilization, and sparse regularization, with finite-sample bounds and sign consistency.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Tree aggregation of compositional data converts independent leaf measurement errors into level-dependent correlated contamination across nodes.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"aa53b9aed38c89a12a3cc1d288d544502583ce38b388b914a38dc4a7edf009a0"},"source":{"id":"2605.15469","kind":"arxiv","version":1},"verdict":{"id":"eb8a33e9-7d92-431c-9141-d2d443103b75","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T14:36:10.211222Z","strongest_claim":"We show that tree aggregation turns leaf-level measurement error into level-dependent, correlated contamination across aggregated nodes... We propose TARCO, which integrates bias-corrected estimating quantities with a tree-aware positive semidefinite stabilization and sparse regularization... We establish finite-sample bounds for prediction and estimation errors and prove sign consistency under conditions that explicitly reflect tree heterogeneity.","one_line_summary":"TARCO corrects measurement-error-induced correlated contamination in tree-aggregated compositional regression via bias-corrected estimating equations, tree-aware PSD stabilization, and sparse regularization, with finite-sample bounds and sign consistency.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The tree structure is prespecified and known, and either the measurement-error covariance is known or a consistent estimator for it is available; the finite-sample bounds and sign consistency hold only under conditions that explicitly reflect tree heterogeneity.","pith_extraction_headline":"Tree aggregation of compositional data converts independent leaf measurement errors into level-dependent correlated contamination across nodes."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15469/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"cited_work_retraction","ran_at":"2026-05-19T15:22:20.541589Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T15:01:17.585987Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T14:49:59.089916Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.090136Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.412088Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.664251Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"88d2d25d7d5602165781071dfdc98122af4265b0f00d55d1c8b438cf85d9550a"},"references":{"count":79,"sample":[{"doi":"","year":1982,"title":"Journal of the Royal Statistical Society: Series B (Methodological) , volume=","work_id":"55d60dd3-b6f9-477c-aab6-9a903262ea06","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2014,"title":"Variable selection in regression with compositional covariates , author=. 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