By Geert Molenberghs, Garrett Fitzmaurice, Michael G. Kenward, Anastasios Tsiatis, Geert Verbeke
Missing info have an effect on approximately each self-discipline by way of complicating the statistical research of gathered information. yet because the Nineties, there were vital advancements within the statistical technique for dealing with lacking info. Written through popular statisticians during this quarter, Handbook of lacking info Methodology offers many methodological advances and the newest purposes of lacking facts tools in empirical research.
Divided into six elements, the instruction manual starts off by way of developing notation and terminology. It experiences the final taxonomy of lacking information mechanisms and their implications for research and provides a ancient standpoint on early equipment for dealing with lacking info. the subsequent 3 elements hide numerous inference paradigms while info are lacking, together with probability and Bayesian equipment; semi-parametric tools, with specific emphasis on inverse chance weighting; and a number of imputation tools.
The subsequent a part of the publication makes a speciality of more than a few ways that verify the sensitivity of inferences to substitute, often non-verifiable assumptions concerning the lacking information method. the ultimate half discusses exact issues, akin to lacking information in medical trials and pattern surveys in addition to methods to version diagnostics within the lacking information atmosphere. In each one half, an creation offers precious historical past fabric and an summary to set the degree for next chapters.
Covering either proven and rising methodologies for lacking facts, this e-book units the scene for destiny study. It offers the framework for readers to delve into study and sensible functions of lacking information methods.
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Extra info for Handbook of Missing Data Methodology
Example text
1 Direct likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stream 2: Inferential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Rubin’s classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Missing at random and the conditional predictive distribution .
J. A. and Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: Wiley. Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd ed. New York: Wiley. Little, R. J. A. and Wang, Y. (1996). Pattern-mixture models for multivariate incomplete data with covariates. Biometrics 52, 98–111. , and Lipsitz, S. R. (1999). A pattern-mixture odds ratio model for incomplete categorical data. Communications in Statistics: Theory and Methods 28, 2843–2869. , Goetghebeur, E.
1 Rubin’s classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Missing at random and the conditional predictive distribution . . . . . . . . . 3 Not missing at random and selection models . . . . . . . . . . . . . . . . . . . Stream 3: Semi-Parametric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Critique of Ad Hoc Methods . . . . . . . . . . . .