On the testability of coarsening assumptions: a hypothesis test for subgroup independence
Plass, J.; Cattaneo, M.; Schollmeyer, G.; Augustin, T.
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|
|Journal||International journal of approximate reasoning|
|Peer Reviewed||Peer Reviewed|
|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|
|Keywords||Coarse data; Missing data; Coarsening at random (CAR); Likelihood-ratio test; Partial identification; Sensitivity analysis|
|Additional Information||This is a description of an article accepted for future publication in: International journal of approximate reasoning, 2017, v.90.|
© 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/