By Edmund Runggaldier
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Extra resources for Carnap's Early Conventionalism: An Inquiry into the Historical Background of the Vienna Circle (Studien Zur Oesterreichischen Philosophie)
As usual, this relies on an assumed distribution of the missing data given the observed data, which cannot be definitively validated from the observed data. Again, this information can be incorporated using for example multiple imputation or EM-type algorithms. Similarly, if X is MAR but the mechanism depends on another variable W which is not in our model of interest, but which is associated with Y, then the complete 2 Statistical Modelling of Partially Observed Data Using Multiple Imputation.
Cole, S. R. (2008). Use of multiple imputation in the epidemiologic literature. American Journal of Epidemiology, 168, 355–357. Little, R. J. , & Rubin, D. B. (2002). ). Chichester: Wiley. Louis, T. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B, 44, 226–233. , & Woodbury, M. (1972). A missing information principle: theory and applications. In L. M. L. Cam, J. Neyman, & E. L. ), Proceedings of the Sixth Berkely Symposium on Mathematics, Statistics and Probability: Vol.
The last possibility is that the response Y is MNAR. From the discussion in the previous Section it should be clear that a complete case analysis will be inconsistent here. e. e. the regression relationship in individuals where Y is missing). Only given such an assumption can we estimate the regression parameters relating Y to X and Z. 2 Covariate X Partially Observed We again consider the regression of Y on X, Z but now suppose that Y, Z are fully observed and X is partially observed. If X is MCAR, then the complete case analysis will be unbiased, as above.
Carnap's Early Conventionalism: An Inquiry into the Historical Background of the Vienna Circle (Studien Zur Oesterreichischen Philosophie) by Edmund Runggaldier