A Censored Mixture Model for Modeling Risk Taking

Nienke Dijkstra*, Henning Tiemeier, Bernd Figner, PJF (Patrick) Groenen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
36 Downloads (Pure)

Abstract

Risk behavior has substantial consequences for health, well-being, and general behavior. The association between real-world risk behavior and risk behavior on experimental tasks is well documented, but their modeling is challenging for several reasons. First, many experimental risk tasks may end prematurely leading to censored observations. Second, certain outcome values can be more attractive than others. Third, a priori unknown groups of participants can react differently to certain risk-levels. Here, we propose the censored mixture model which models risk taking while dealing with censoring, attractiveness to certain outcomes, and unobserved individual risk preferences, next to experimental conditions.
Original languageEnglish
Pages (from-to)1103-1129
Number of pages27
JournalPsychometrika
Volume87
Issue number3
DOIs
Publication statusPublished - 10 Feb 2022

Bibliographical note

Funding Information:
Supercomputing computations for the censored mixture model are supported by the NWO Physical Science Division (Exacte Wetenschapen) and SURFsara (Lisa compute cluster, www.surfsara.nl ). We gratefully acknowledge the contribution of children and parents, general practitioners, hospitals, midwives, and pharmacies in Rotterdam, involved in the Generation R Study. The work of Nienke Dijkstra was supported by the Research Excellence Initiative (REI) of the Erasmus University Rotterdam grant awarded to Patrick Groenen and Henning Tiemeier with applicants Roy Thurik and Ingmar Franken, Project Number 265.403. Furthermore, the work of Henning Tiemeier was supported by the Netherlands Organisation for Health Research and Development (ZonMw) VICI (project 016.VICI.170.200, awarded to Henning Tiemeier). Last, we thank three anonymous reviewers and the associate editor for their valuable feedback.

Publisher Copyright:
© 2022, The Author(s).

Fingerprint

Dive into the research topics of 'A Censored Mixture Model for Modeling Risk Taking'. Together they form a unique fingerprint.

Cite this