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Discussion on “The central role of the identifying assumption in population size estimation” by Serge Aleshin-Guendel, Mauricio Sadinle, and Jon Wakefield

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Discussion on “The central role of the identifying assumption in population size estimation” by Serge Aleshin-Guendel, Mauricio Sadinle, and Jon Wakefield. / King, Ruth; McCrea, Rachel; Overstall, Antony.
In: Biometrics, Vol. 80, No. 1, ujad032, 29.01.2024.

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@article{7050d98c833341c0a87a066144046a57,
title = "Discussion on “The central role of the identifying assumption in population size estimation” by Serge Aleshin-Guendel, Mauricio Sadinle, and Jon Wakefield",
abstract = "In this discussion response, we consider some practical implications of the authors{\textquoteright} consideration of the no-highest-order interaction (NHOI) model for multiple systems estimation, which permits the authors to derive the explicit (albeit untestable) identifying assumption related to the unobserved (or missing) individuals. In particular, we discuss several aspects, from the standard process of model selection to potential poor predictive performance due to over-fitting and the implications of data reduction. We discuss these aspects in relation to the case study presented by the authors relating to the number of civilian casualties within the Kosovo war, and conduct further preliminary simulations to investigate these issues further. The results suggest that the NHOI models considered, despite having a potentially useful theoretical result in relation to the underlying identifying assumption, may perform poorly in practice.",
author = "Ruth King and Rachel McCrea and Antony Overstall",
year = "2024",
month = jan,
day = "29",
doi = "10.1093/biomtc/ujad032",
language = "English",
volume = "80",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - Discussion on “The central role of the identifying assumption in population size estimation” by Serge Aleshin-Guendel, Mauricio Sadinle, and Jon Wakefield

AU - King, Ruth

AU - McCrea, Rachel

AU - Overstall, Antony

PY - 2024/1/29

Y1 - 2024/1/29

N2 - In this discussion response, we consider some practical implications of the authors’ consideration of the no-highest-order interaction (NHOI) model for multiple systems estimation, which permits the authors to derive the explicit (albeit untestable) identifying assumption related to the unobserved (or missing) individuals. In particular, we discuss several aspects, from the standard process of model selection to potential poor predictive performance due to over-fitting and the implications of data reduction. We discuss these aspects in relation to the case study presented by the authors relating to the number of civilian casualties within the Kosovo war, and conduct further preliminary simulations to investigate these issues further. The results suggest that the NHOI models considered, despite having a potentially useful theoretical result in relation to the underlying identifying assumption, may perform poorly in practice.

AB - In this discussion response, we consider some practical implications of the authors’ consideration of the no-highest-order interaction (NHOI) model for multiple systems estimation, which permits the authors to derive the explicit (albeit untestable) identifying assumption related to the unobserved (or missing) individuals. In particular, we discuss several aspects, from the standard process of model selection to potential poor predictive performance due to over-fitting and the implications of data reduction. We discuss these aspects in relation to the case study presented by the authors relating to the number of civilian casualties within the Kosovo war, and conduct further preliminary simulations to investigate these issues further. The results suggest that the NHOI models considered, despite having a potentially useful theoretical result in relation to the underlying identifying assumption, may perform poorly in practice.

U2 - 10.1093/biomtc/ujad032

DO - 10.1093/biomtc/ujad032

M3 - Journal article

VL - 80

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 1

M1 - ujad032

ER -