Pith Number

pith:3ULXRGIS

pith:2025:3ULXRGISXDZBKGJHG7IHV4IWJE

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The $\alpha$--regression for compositional data: a unified framework for standard, temporal and spatial regression models including compositional predictors

Abdulaziz Alenazi, Michail Tsagris, Nader Alharbi, Yannis Pantazis

α-regression uses a data-driven power transform to unify standard, temporal and spatial models for compositional data.

arxiv:2510.12663 v7 · 2025-10-14 · stat.ME

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\usepackage{pith}
\pithnumber{3ULXRGISXDZBKGJHG7IHV4IWJE}

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Applications to four real datasets illustrate that the models perform on par with or outperform existing models in the literature. The examples showcase that spatial extensions capture the dependence and improve the predictive performance. Overall, the examples provide evidence that the log-ratio methodology does not lead to the optimal results.

C2weakest assumption

The power transformation with data-driven α is a valid and superior modeling choice for compositional data that preserves the necessary constraints while allowing the non-linear least squares formulation and spatial extensions to produce reliable marginal effects and predictions.

C3one line summary

The α-regression framework unifies standard, temporal, and spatial regression for compositional data via a data-driven power transformation and shows competitive or better performance than existing methods on real datasets.

Receipt and verification

First computed	2026-06-05T01:15:16.974407Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

dd17789912b8f215192737d07af1164902961d1eac943f50bb188d7e3080c636

Aliases

arxiv: 2510.12663 · arxiv_version: 2510.12663v7 · doi: 10.48550/arxiv.2510.12663 · pith_short_12: 3ULXRGISXDZB · pith_short_16: 3ULXRGISXDZBKGJH · pith_short_8: 3ULXRGIS

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3ULXRGISXDZBKGJHG7IHV4IWJE \
  | 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: dd17789912b8f215192737d07af1164902961d1eac943f50bb188d7e3080c636

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0cad8b4f0fdd96027398516dec4d2be12d94bfd3b1ddcd5c28018575318ac0c0",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2025-10-14T15:59:02Z",
    "title_canon_sha256": "58fcf85a90e8ed92d96928db0a9db67641b5251647416d2be886acc57f00b3b2"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2510.12663",
    "kind": "arxiv",
    "version": 7
  }
}