{
  "abstract": "Pooling many channels with a shared unknown input can leave persistent bias when each channel retains a fixed number of observations and its own incidental parameters. For heterogeneous smooth channel manifolds under isotropic noise, the second-order bias is an information-weighted regression of mean curvature on normal input velocity. A feasible correction gives centred Gaussian inference as the noise level $σ$ decreases with $Rσ^6\\to0$, compared with $Rσ^2\\to0$ without correction. We extend the stochastic expansion and studentization to deterministic channel arrays with densely correlated Gaussian errors of bounded covariance operator norm. General marginal covariance adds a forcing term that can bias even flat channels, while cross-channel covariance changes uncertainty. For Gaussian errors with a separable frame-by-channel covariance whose channel factor is autoregressive in a known ordering, an independent zero-mean calibration field yields a covariance estimator accurate enough for corrected inference. Boundary accumulation and fixed working population models introduce distinct bias terms; rational-multiplier and zero-count arguments connect identification to frame-integrated compartment models. Simulations test both oracle and estimated-covariance inference, including independent PET replications. Moderate-noise undercoverage, optimization ambiguity and the need to validate reconstruction covariance limit practical inference. The real-data example supports sensitivity analysis, not clinically calibrated intervals.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.33642",
  "author_total": 1,
  "authors": [
    {
      "name": "Hsin-Hsiung Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5pq6nm6715yyn.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.33642",
  "fulltext_url": null,
  "id": "5pq6nm6715yyn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.33642",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.ST"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.TH"
    ],
    [
      "openalex",
      "2207|Control Systems and Identification"
    ]
  ],
  "openalex": "W7223826315",
  "pdf_url": "https://arxiv.org/pdf/2610.33642",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5pq6nm6715yyn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:40Z",
      "native_id": "2610.33642",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.33642"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.ST",
      "subfield": "statistics"
    }
  ],
  "title": "Identification and curvature bias for shared inputs in multichannel dynamical models",
  "title_text": "Identification and curvature bias for shared inputs in multichannel dynamical models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:49Z",
  "url": "https://research.draconitic.com/paper/5pq6nm6715yyn",
  "venue": "arXiv",
  "versions": []
}