Inference on data with both multiplicative and additive measurement errors.

Yuxiang Zong, Yinfu Liu, Yanyuan Ma, Ingrid Van Keilegom

Abstract read

In one paragraph

Article in Scandinavian journal of statistics, theory and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuxiang ZongResearch Centre for Operations Research and Statistics, KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium.

Yinfu LiuDepartment of Data Analytics and Digitalization, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands.

Yanyuan MaDepartment of Statistics, Pennsylvania State University, University Park, PA 16802, USA.

Ingrid Van KeilegomResearch Centre for Operations Research and Statistics, KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium.

Funding

Landis Award for Outstanding Mentorship: Administrative Supplement to NS131225R01NS131225 · NINDS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Tanya Pamela Garcia · 2023 to 2026

$2.2M

NINDS NIH HHS R01 NS131225

6 · The paper itself

Abstract

Measurement errors are omnipresent in many fields and can lead to serious problems in statistical analysis. In the literature, measurement errors are often assumed to be either additive or multiplicative. We consider the case where a variable is subject to both additive and multiplicative errors. We establish the identifiability and propose a moment-based estimator for the variances of these two types of errors, which is shown to be consistent. We further derive the asymptotic distribution of the estimator and conduct hypothesis tests to examine the existence of the two types of errors. We also develop a likelihood-based method to approximate the density of the error-prone variable. We apply our strategy in the context of linear regression and study its effect on the estimation of regression parameters in combination with Regression Calibration and Simulation Extrapolation. The proposed methodology is numerically investigated through simulations and is implemented in a real data application.

Indexed as

Bernstein polynomialMeasurement errorMethod of momentsRegression calibrationSimulation extrapolation

Identifiers

PMID41789348

DOI10.1111/sjos.70009

PMCPMC12959484

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.