Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4945198 | International Journal of Approximate Reasoning | 2017 | 15 Pages |
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 first re-illustrate the impossibility to test CAR and then contrast it to another type of coarsening called subgroup independence (SI). 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. The results are illustrated by the data of the German Panel Study “Labour Market and Social Security” (PASS).
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
J. Plass, M. Cattaneo, G. Schollmeyer, T. Augustin,