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Censoring heavy-tail count distributions for parameter estimation with an application to stable distributions

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Antonio Di Noia, Marzia Marcheselli, Caterina Pisani, Luca Pratelli

Statistics Theory · Submitted 22 Dec 2022 · Published version

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim A new approach based on censoring and moment criterion is introduced for parameter estimation of count distributions when the probability generating function is available even though a closed form of the probability mass function and/or finite moments do not exist.

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arXiv ID2212.11697 (version 4)
Pith ID3FZPLXR3, Pith's permanent record of this paper and its review
CitationAntonio Di Noia, Caterina Pisani, Luca Pratelli, Marzia Marcheselli. "Censoring heavy-tail count distributions for parameter estimation with an application to stable distributions." arXiv:2212.11697 (2022). https://pith.science/paper/3FZPLXR3
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 1,647 input tokens.

What the abstract itself states, as probabilities: claims first 0.11 · states limitation 0.23 · human participants 0.03 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.07 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v4. Results and the main claim are underlined; .

A new approach based on censoring and moment criterion is introduced for parameter estimation of count distributions when the probability generating function is available even though a closed form of the probability mass function and/or finite moments do not exist.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 A new approach based on censoring and moment criterion is introduced for parameter estimation of count distributions when the probability generating function is available even though a closed form of the probability mass function and/or finite moments do not exist. contribution 0.97 0.87 0.53 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from proof or derivation 0.71.

What was examined, and what was not

sources read
  • arXiv listing abstract v4
not examined
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  • figures and tables
  • references and citing sentences
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

A new approach based on censoring and moment criterion is introduced for parameter estimation of count distributions when the probability generating function is available even though a closed form of the probability mass function and/or finite moments do not exist.

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