Uncertainty intervals for regression parameters with non-ignorable missingness in the outcome
2014 (English)In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798, Vol. 56, no 3, 829-847 p.Article in journal (Refereed) Published
When estimating regression models with missing outcomes, scientists usually have to rely either on a missing at random assumption (missing mechanism is independent from the outcome given the observed variables) or on exclusion restrictions (some of the covariates affecting the missingness mechanism do not affect the outcome). Both these hypotheses are controversial in applications since they are typically not testable from the data. The alternative, which we pursue here, is to derive identification sets (instead of point identification) for the parameters of interest when allowing for a missing not at random mechanism. The non-ignorability of this mechanism is quantified with a parameter. When the latter can be bounded with a priori information, a bounded identification set follows. Our approach allows the outcome to be continuous and unbounded and relax distributional assumptions. Estimation of the identification sets can be performed via ordinary least squares and sampling variability can be incorporated yielding uncertainty intervals achieving a coverage of at least (1-α) probability. Our work is motivated by a study on predictors of body mass index (BMI) change in middle age men allowing us to identify possible predictors of BMI change even when assuming little on the missing mechanism.
Place, publisher, year, edition, pages
Springer-Verlag New York, 2014. Vol. 56, no 3, 829-847 p.
Heckman model, informative dropout, selection models, sensitivity analysis, set identification, two stage least squares
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:umu:diva-92843DOI: 10.1007/s00362-014-0610-xISI: 000358219900013ScopusID: 2-s2.0-84904239372OAI: oai:DiVA.org:umu-92843DiVA: diva2:743926
FunderSwedish Research Council