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
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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 advantages.
Krishnan Bhaskaran, Liam Smeeth
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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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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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Outcome-sensitive multiple imputation: a simulation study [PDF]
BMC Medical Research Methodology, 2017 Background Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should
Evangelos Kontopantelis +3 more
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Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study [PDF]
American Journal of Epidemiology, 2014 Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research. The "true" imputation model may contain nonlinearities which are not included in default imputation models. Random forest imputation
Anoop D Shah +2 more
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On using a non-probability sample for the estimation of population parameters
Lietuvos Matematikos Rinkinys, 2023 We aim to find a way to effectively integrate a non-probability (voluntary) sample under the data framework, where the study variable is also observed in a probability sample of some statistical survey.
Ieva Burakauskaitė, Andrius Čiginas
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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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A Tutorial for Handling Suspected Missing Not at Random Data in Longitudinal Clinical Trials [PDF]
Tutorials in Quantitative Methods for Psychology, 2023 Missing data in longitudinal randomized clinical trials, even if assumed to be missing at random (MAR), can result in biased parameter estimates and incorrect treatment conclusions.
Peugh, James L. +2 more
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