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Testing of coarsening mechanisms: Coarsening at random versus subgroup independence

Cattaneo, Marco E. G. V.; Augustin, Thomas; Plass, Julia; Schollmeyer, Georg

Authors

Marco E. G. V. Cattaneo

Thomas Augustin

Julia Plass

Georg Schollmeyer



Abstract

Abstract Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at random (CAR) to force them to be single-valued. Using the PASS data as an example, we re-illustrate the impossibility to test CAR and contrast it to another type of uninformative coarsening called subgroup independence (SI). It turns out that SI is testable.

Citation

Cattaneo, M. E. G. V., Augustin, T., Plass, J., & Schollmeyer, G. (2017). Testing of coarsening mechanisms: Coarsening at random versus subgroup independence. Advances in Intelligent Systems and Computing, 456, 415-422. https://doi.org/10.1007/978-3-319-42972-4_51

Journal Article Type Article
Acceptance Date Jun 1, 2016
Online Publication Date Jul 30, 2016
Publication Date Jan 1, 2017
Deposit Date Jun 17, 2016
Journal Soft methods for data science
Print ISSN 2194-5357
Electronic ISSN 2194-5357
Publisher Springer Verlag
Peer Reviewed Not Peer Reviewed
Volume 456
Pages 415-422
Book Title Advances in Intelligent Systems and Computing; Soft Methods for Data Science
ISBN 978-3-319-42971-7
DOI https://doi.org/10.1007/978-3-319-42972-4_51
Keywords Coarse data, Missing data, Coarsening at random (CAR), Hypothesis testing, Likelihood-ratio test
Public URL https://hull-repository.worktribe.com/output/439796
Contract Date Jun 17, 2016