Statistical Analysis with Measurement Error or Misclassification by Grace Y. Yi

Author:Grace Y. Yi
Language: eng
Format: epub
Publisher: Springer New York, New York, NY

5.5.4 Simultaneous Inference to Accommodating Missingness and Measurement Error Effects

We discuss a strategy that simultaneously adjusts for effects induced from measurement error and missingness, which is accomplished using the likelihood-based methods. We consider the case where the response process is modeled as (5.9) and (5.10), which is denoted as f(y i | x i , z i , u i ; β) together with the model f(u i ; γ) for random effects u i . Here β and γ are the associated model parameters, and the dispersion parameter ϕ is treated as known for ease exposition.

For missing data processes, we consider the scenario where the process is true-covariate-driven and the missing data indicator R i is independent of random effects u i when {Y i , X i , X i ∗, Z i } is given, i.e.,

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