Results 221 to 230 of about 211,055 (241)
A hierarchical conformal framework for uncertainty-aware length of stay prediction in multi-hospital settings. [PDF]
Sci RepShahbazi MA, Baheri A, Azadeh-Fard N.
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Amotivation and Academic Engagement in Western Romanian University Students: A Conditional Self-Regulation Model with Forethought and Self-Reflection Under Perceived Performance Control. [PDF]
Brain SciRoman A +10 more
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Estimating average causal effects with incomplete exposure and confounders. [PDF]
J Causal InferenceWen L, McGee G.
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Statistics in Medicine, 2009
AbstractMultiple imputation (MI) has increasingly received attention as a flexible tool to resolve missing data problems both in observational and controlled studies. Our goal has been to develop a valid and efficient MI procedure for the Diabetes Prediction and Prevention Nutrition Study, in which the diet of a cohort of newborn children with HLA‐DQB1‐
Jaakko Nevalainen, Suvi M Virtanen
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AbstractMultiple imputation (MI) has increasingly received attention as a flexible tool to resolve missing data problems both in observational and controlled studies. Our goal has been to develop a valid and efficient MI procedure for the Diabetes Prediction and Prevention Nutrition Study, in which the diet of a cohort of newborn children with HLA‐DQB1‐
Jaakko Nevalainen, Suvi M Virtanen
exaly +3 more sources
Journal of Educational and Behavioral Statistics, 2017
Multiple imputation methods can generally be divided into two broad frameworks: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution, whereas FCS imputes variables one at a time from a series of univariate conditional ...
Stephen A. Mistler, Craig K. Enders
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Multiple imputation methods can generally be divided into two broad frameworks: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution, whereas FCS imputes variables one at a time from a series of univariate conditional ...
Stephen A. Mistler, Craig K. Enders
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Psychological Methods, 2018
Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables, differential ...
Brian T Keller, Roy Levy, Craig Enders
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Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables, differential ...
Brian T Keller, Roy Levy, Craig Enders
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Multiple imputation of discrete and continuous data by fully conditional specification
Statistical Methods in Medical Research, 2007The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any knowledge about the process that generated the missing data.
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Multiple Imputation for Multivariate Missing Data: The Fully Conditional Specification Approach
2021Yulei He, Guangyu Zhang, Chiu-Hsieh Hsu
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