Common Methodological Challenges Encountered With Multiple Systems Estimation Studies

Dados Bibliográficos

ID21308860
AutoresKyle Vincent (0000-0002-3567-0798, Independent Researcher and Consultant, Ottawa, ON, Canada, autor correspondente), Serveh Sharifi Far (0000-0001-8403-6286, The University of Edinburgh School of Mathematics, UK), Michail Papathomas (0000-0002-5897-695X, University of St Andrews)
Ano2023
Volume69
Fascículo12
Páginas2561-2573
Data de publicação2023-11-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoCrime & Delinquency (JOURNAL)
Identificadores do periódicoISSN: 0011-1287 • E-ISSN: 1552-387X
EditoraSAGE Publications (PUBLISHER • US)
DOI10.1177/0011128720981900
OpenAlexW3118190275
IdiomaEN
Citações recebidas2
Referências citadas23

Multiple systems estimation refers to a class of inference procedures that are commonly used to estimate the size of hidden populations based on administrative lists. In this paper we discuss some of the common challenges encountered in such studies. In particular, we summarize theoretical issues relating to the existence of maximum likelihood estimators, model identifiability, and parameter redundancy when there is sparse overlap among the lists. We also discuss techniques for matching records when there are no unique identifiers, exploiting covariate information to improve estimation, and addressing missing data. We offer suggestions for remedial actions when these issues/challenges manifest. The corresponding R coding packages that can assist with the analyses of multiple systems estimation data sets are also discussed

Coding (social sciences) · Covariate · Data mining · Data science · Estimation · Estimator · Identifiability · Identifier · Inference · Machine learning · Maximum likelihood · Missing data · Redundancy (engineering) · Statistics · Artificial Intelligence · Census and Population Estimation · Computer Science · Data-Driven Disease Surveillance · Engineering · Mathematics · Statistical Methods and Bayesian Inference