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Markov Chain Generated Profile Likelihood Inference under Generalized Proportional to Size Non-ignorable Non-response

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Abstract

We apply two non-ignorable non-response models to the data of the Norwegian Labour Force Survey, the Fertility Survey and the Alveolar Bone Loss Survey. Both models focus on the marginal effect which the object variable of interest has on the non-response, where we assume the probability of non-response to be generalized proportional to the size of the object variable. We draw the inference of the parameter of interest based on the first-order theory of the profile likelihood. We adapt the Markov chain sampling techniques to efficiently generate the profile likelihood inference. We explain and demonstrate why the resampling approach is more flexible for the likelihood inference than under the Beyesian framework.

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  • Ib Thomsen & Li-Chun Zhang & Joseph Sexton, 2000. "Markov Chain Generated Profile Likelihood Inference under Generalized Proportional to Size Non-ignorable Non-response," Discussion Papers 274, Statistics Norway, Research Department.
    • Handle: RePEc:ssb:dispap:274
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    File URL: https://www.ssb.no/a/publikasjoner/pdf/DP/dp274.pdf
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    6. Steven E. Stern, 1997. "A Second‐order Adjustment to the Profile Likelihood in the Case of a Multidimensional Parameter of Interest," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 653-665.
    7. J. G. Ibrahim & S. R. Lipsitz & M.‐H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
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