{
  "abstract": "Education agencies often summarize test score distributions by counting how many students scored in 3 to 5 different \\textit{bins}. The HETOP model transforms bin counts into estimated means and standard deviations, assuming that scores follow a normal distribution within each school or district. Past HETOP implementations ran slowly, taking 3--60 minutes, if they finished, when given bin counts for all 1,151 districts in Texas. Our new function, \\code{fast\\_hetop()} in the R package \\pkg{binest}, runs all Texas districts in less than a second. Users can choose between maximum likelihood, empirical Bayes, sample-based or population-based estimates. Estimates are strongly correlated with true values, but have bias when the score distribution is skewed and scores are concentrated in the lowest or highest bin.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36312",
  "author_total": 1,
  "authors": [
    {
      "name": "Paul T. von Hippel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/edcc30jvkqjba.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36312",
  "fulltext_url": null,
  "id": "edcc30jvkqjba",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36312",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Bayesian Inference"
    ]
  ],
  "openalex": "W7214898362",
  "pdf_url": "https://arxiv.org/pdf/2609.36312",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/edcc30jvkqjba.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:41Z",
      "native_id": "2609.36312",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36312"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Faster Estimates from Binned Test Score Data---and How Accurate They Are",
  "title_text": "Faster Estimates from Binned Test Score Data---and How Accurate They Are",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:41Z",
  "url": "https://research.draconitic.com/paper/edcc30jvkqjba",
  "venue": "arXiv",
  "versions": []
}