Pith Number

pith:WCC6QZJO

pith:2026:WCC6QZJOSLOH5ZGLXB3C7KE4RW

not attested not anchored not stored refs resolved

Tree-aggregated regression for compositional data with measurement errors

Tianying Wang, Zhenghan Li

Tree aggregation of compositional data converts independent leaf measurement errors into level-dependent correlated contamination across nodes.

arxiv:2605.15469 v1 · 2026-05-14 · stat.ME

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Claims

C1strongest 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.

C2weakest 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.

C3one 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.

References

79 extracted · 79 resolved · 0 Pith anchors

[1] Journal of the Royal Statistical Society: Series B (Methodological) , volume= 1982

[2] Variable selection in regression with compositional covariates , author=. Biometrika , volume=. 2014 , publisher= 2014

[3] Scientific Reports , volume= 2021

[4] It's all relative: Regression analysis with compositional predictors , author=. Biometrics , volume=. 2023 , publisher= 2023

[5] High-dimensional statistics: A non-asymptotic viewpoint , author=. 2019 , publisher= 2019

Receipt and verification

First computed	2026-05-20T00:01:00.157759Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b085e8652e92dc7ee4cbb8762fa89c8da2f7406fd42607c682c9bf11479406a1

Aliases

arxiv: 2605.15469 · arxiv_version: 2605.15469v1 · doi: 10.48550/arxiv.2605.15469 · pith_short_12: WCC6QZJOSLOH · pith_short_16: WCC6QZJOSLOH5ZGL · pith_short_8: WCC6QZJO

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/WCC6QZJOSLOH5ZGLXB3C7KE4RW \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b085e8652e92dc7ee4cbb8762fa89c8da2f7406fd42607c682c9bf11479406a1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a5cdd99cefbf89c931120fee28d877129964c9c498c0e77de258a7568bdf386f",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-14T23:16:34Z",
    "title_canon_sha256": "33189d8cbac7870de5f18848519ced4eac522bb5867c91119353861a3235eda4"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15469",
    "kind": "arxiv",
    "version": 1
  }
}