Common methodological challenges encountered with multiple systems estimation studies

Kyle Vincent, Serveh Sharifi Far, Michail Papathomas

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
13 Downloads (Pure)

Abstract

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.
Original languageEnglish
Number of pages13
JournalCrime and Delinquency
VolumeOnlineFirst
Early online date22 Dec 2020
DOIs
Publication statusE-pub ahead of print - 22 Dec 2020

Keywords

  • Covariate information
  • Local MSE challenges
  • Matching records
  • Missing observations
  • Model identifiability

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