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What is the difference between missing completely at random and missing at random? [PDF]
International Journal of Epidemiology, 2014 The terminology describing missingness mechanisms is confusing. In particular the meaning of 'missing at random' is often misunderstood, leading researchers faced with missing data problems away from multiple imputation, a method with considerable ...
Krishnan Bhaskaran, Liam Smeeth
exaly +7 more sources
Block-Conditional Missing at Random Models for Missing Data [PDF]
Statistical Science, 2010 Two major ideas in the analysis of missing data are (a) the EM algorithm [Dempster, Laird and Rubin, J. Roy. Statist. Soc. Ser. B 39 (1977) 1--38] for maximum likelihood (ML) estimation, and (b) the formulation of models for the joint distribution of the
John D. Kalbfleisch +3 more
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Model Selection with Missing Data Embedded in Missing-at-Random Data
Stats, 2023 When models are built with missing data, an information criterion is needed to select the best model among the various candidates. Using a conventional information criterion for missing data may lead to the selection of the wrong model when data are not ...
Keiji Takai, Kenichi Hayashi
doaj +3 more sources
What Is Meant by “Missing at Random”?
Statistical Science, 2013 The concept of missing at random is central in the literature on statistical analysis with missing data. In general, inference using incomplete data should be based not only on observed data values but should also take account of the pattern of missing values.
Shaun R Seaman +2 more
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The Efficiency of Missing at Random Planned Missing Designs
MathematicsPlanned Missing Designs (PMDs) allow for different sets or patterns of variables to be collected from sample units. While the typical motivation for PMDs is to manage respondent burden, they can also reduce data collection costs and provide flexibility ...
David G. Steel, James Chipperfield
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Bayesian Estimation of Log-Normal Distribution Under Ranked Set Sampling With Missing Data
IEEE Access, 2021 In this paper, joint Bayesian estimation of two parameters of a log-normal distribution is obtained based on simple random sampling (SRS) and ranked set sampling (RSS) with complete and missing data.
Fengxi Zong, Rubing Li
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Joint Models for Incomplete Longitudinal Data and Time-to-Event Data
Mathematics, 2022 Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data.
Yuriko Takeda +2 more
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Lithuanian Journal of Statistics, 2022 Our study presents the methods adopted to produce accurate imputed values for Africa's food security and nutrition (FSN). We focused primarily on the following five imputation methods for handling missing data: Mean Imputation; Multiple Imputed values ...
Adusei Bofa, Temesgen Zewotir
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Impact on Cronbach's alpha of simple treatment methods for missing data [PDF]
Tutorials in Quantitative Methods for Psychology, 2016 The scientific treatment of missing data has been the subject of research for nearly a century. Strangely, interest in missing data is quite new in the fields of educational science and psychology (Peugh & Enders, 2004; Schafer & Graham, 2002). It is now
Béland, Sébastien +2 more
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Missing at random: a stochastic process perspective [PDF]
Biometrika, 2021 SummaryWe offer a natural and extensible measure-theoretic treatment of missingness at random. Within the standard missing-data framework, we give a novel characterization of the observed data as a stopping-set sigma algebra. We demonstrate that the usual missingness-at-random conditions are equivalent to requiring particular stochastic processes to be
Farewell, DM, Daniel, RM, Seaman, SR
openaire +4 more sources