RT Article
T1 Common Methodological Challenges Encountered With Multiple Systems Estimation Studies
JF Crime & delinquency
VO 69
IS 12
SP 2561
OP 2573
A1 Vincent, Kyle Shane
A2 Sharifi Far, Serveh
A2 Papathomas, Michail
LA English
YR 2023
UL https://krimdok.uni-tuebingen.de/Record/1869155297
AB 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.
K1 covariate information
K1 local MSE challenges
K1 matching records
K1 missing observations
K1 model identifiability
DO 10.1177/0011128720981900