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Nonparametric Simulation Extrapolation for Measurement Error Models

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Dylan Spicker, Michael Wallace, Grace Yi

Methodology · Submitted 4 Nov 2021

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

The authors' main claim We propose an extension to the simulation extrapolation method which is nonparametric in the sense that no specific distributional assumptions are required on the error terms.

how it is stated · asserted 1.00 read the record →

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arXiv ID2111.02863 (version 3)
Pith IDTVFRAI6N, Pith's permanent record of this paper and its review
CitationDylan Spicker, Grace Yi, Michael Wallace. "Nonparametric Simulation Extrapolation for Measurement Error Models." arXiv:2111.02863 (2021). https://pith.science/paper/TVFRAI6N
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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, 5,460 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.45 · human participants 0.07 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.27 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

The presence of measurement error is a widespread issue which, when ignored, can render the results of an analysis unreliable. Numerous corrections for the effects of measurement error have been proposed and studied, often under the assumption of a normally distributed, additive measurement error model. One such method is simulation extrapolation, or SIMEX. In many situations observed data are non-symmetric, heavy-tailed, or otherwise highly non-normal. In these settings, correction techniques relying on the assumption of normality are undesirable. We propose an extension to the simulation extrapolation method which is nonparametric in the sense that no specific distributional assumptions are required on the error terms. The technique is implemented when either validation data or replicate measurements are available, and is designed to be immediately accessible for those familiar with simulation extrapolation.

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 The presence of measurement error is a widespread issue which, when ignored, can render the results of an analysis unreliable. background 1.00 0.04 0.03 asserted 1.00
s1 Numerous corrections for the effects of measurement error have been proposed and studied, often under the assumption of a normally distributed, additive measurement error model. background 1.00 0.03 0.03 asserted 1.00
s2 One such method is simulation extrapolation, or SIMEX. background 0.99 0.04 0.04 asserted 1.00
s3 In many situations observed data are non-symmetric, heavy-tailed, or otherwise highly non-normal. background 0.89 0.10 0.02 asserted 0.91
s4 In these settings, correction techniques relying on the assumption of normality are undesirable. background 0.56 0.05 0.05 asserted 0.97
s5 We propose an extension to the simulation extrapolation method which is nonparametric in the sense that no specific distributional assumptions are required on the error terms. contribution 0.99 0.93 0.60 asserted 1.00
s6 The technique is implemented when either validation data or replicate measurements are available, and is designed to be immediately accessible for those familiar with simulation extrapolation. method 0.87 0.76 0.13 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.60.

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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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

The presence of measurement error is a widespread issue which, when ignored, can render the results of an analysis unreliable. Numerous corrections for the effects of measurement error have been proposed and studied, often under the assumption of a normally distributed, additive measurement error model. One such method is simulation extrapolation, or SIMEX. In many situations observed data are non-symmetric, heavy-tailed, or otherwise highly non-normal. In these settings, correction techniques relying on the assumption of normality are undesirable. We propose an extension to the simulation extrapolation method which is nonparametric in the sense that no specific distributional assumptions are required on the error terms. The technique is implemented when either validation data or replicate measurements are available, and is designed to be immediately accessible for those familiar with simulation extrapolation.

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