Missing Data: A Unified Taxonomy Guided by Conditional Independence
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Abstract
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DOI: 10.1111/insr.12242
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- Doretti, Marco & Geneletti, Sara & Stanghellini, Elena, 2018. "Missing data: a unified taxonomy guided by conditional independence," LSE Research Online Documents on Economics 87227, London School of Economics and Political Science, LSE Library.
References listed on IDEAS
- M. G. Kenward, 2003. "Pattern-mixture models with proper time dependence," Biometrika, Biometrika Trust, vol. 90(1), pages 53-71, March.
- Geert Molenberghs & Caroline Beunckens & Cristina Sotto & Michael G. Kenward, 2008. "Every missingness not at random model has a missingness at random counterpart with equal fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 371-388, April.
- Fabrizia Mealli & Donald B. Rubin, 2015. "Clarifying missing at random and related definitions, and implications when coupled with exchangeability," Biometrika, Biometrika Trust, vol. 102(4), pages 995-1000.
- G. Molenberghs & B. Michiels & M. G. Kenward & P. J. Diggle, 1998. "Monotone missing data and pattern‐mixture models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 52(2), pages 153-161, June.
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Cited by:
- Nitzan Cohen & Yakir Berchenko, 2021. "Normalized Information Criteria and Model Selection in the Presence of Missing Data," Mathematics, MDPI, vol. 9(19), pages 1-23, October.
- Mehboob Ali & Göran Kauermann, 2021. "A split questionnaire survey design in the context of statistical matching," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1219-1236, October.
- Thakur Narendra Singh & Shukla Diwakar, 2022. "Missing data estimation based on the chaining technique in survey sampling," Statistics in Transition New Series, Polish Statistical Association, vol. 23(4), pages 91-111, December.
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JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
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