Skip to main content

Research Repository

See what's under the surface

On the testability of coarsening assumptions: a hypothesis test for subgroup independence

Plass, J.; Cattaneo, M.; Schollmeyer, G.; Augustin, T.

Authors

J. Plass

M. Cattaneo

G. Schollmeyer

T. Augustin



Abstract

Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at random (CAR) to force them to be single-valued. Focusing on a coarse categorical response variable and a precisely observed categorical covariate, we re-illustrate the impossibility to test CAR and contrast it to another type of coarsening called subgroup independence (SI), using the data of the German Panel Study ``Labour Market and Social Security'' as an example. It turns out that -- depending on the number of subgroups and categories of the response variable -- SI can be point-identifying as CAR, but testable unlike CAR. A main goal of this paper is the construction of the likelihood-ratio test for SI. All issues are similarly investigated for the here proposed generalized versions, gCAR and gSI, thus allowing a more flexible application of this hypothesis test.

Journal Article Type Article
Publication Date 2017-11
Journal International journal of approximate reasoning
Print ISSN 0888-613X
Electronic ISSN 0888-613X
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 90
Pages 292-306
APA6 Citation Plass, J., Cattaneo, M., Schollmeyer, G., & Augustin, T. (2017). On the testability of coarsening assumptions: a hypothesis test for subgroup independence. International Journal of Approximate Reasoning, 90, 292-306. https://doi.org/10.1016/j.ijar.2017.07.014
DOI https://doi.org/10.1016/j.ijar.2017.07.014
Keywords Coarse data; Missing data; Coarsening at random (CAR); Likelihood-ratio test; Partial identification; Sensitivity analysis
Publisher URL http://www.sciencedirect.com/science/article/pii/S0888613X17304905
Additional Information This is a description of an article accepted for future publication in: International journal of approximate reasoning, 2017, v.90.

Files

Article (942 Kb)
PDF

Copyright Statement
© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/





Downloadable Citations