Results 91 to 100 of about 762,527 (191)
An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout
Journal of Probability and Statistics, 2019 In any longitudinal study, a dropout before the final timepoint can rarely be avoided. The chosen dropout model is commonly one of these types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR), and Shared ...
Amal Almohisen +2 more
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MICE vs PPCA: Missing data imputation in healthcare
Informatics in Medicine Unlocked, 2019 Retrospective analyses of real-world clinical data face challenges owing to the absence of some data elements. Historically, missing data was addressed by first classifying its presence into one of three categories: missing completely at random (MCAR ...
Harshad Hegde +5 more
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Statistical guarantees for the EM algorithm: From population to sample-based analysis [PDF]
, 2014 We develop a general framework for proving rigorous guarantees on the performance of the EM algorithm and a variant known as gradient EM. Our analysis is divided into two parts: a treatment of these algorithms at the population level (in the limit of ...
Balakrishnan, Sivaraman +2 more
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Messy Data Modelling in Health Care Contingent Valuation Studies [PDF]
This study addresses the complexity in modeling contingent valuation surveys with true zeros and non-ignorable missing responses including “don’t knows†and protest responses. An endogenous switching tobit model is specified to simultaneously estimate
Kostas Mavromaras +2 more
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Соціологічні студіїMissing categorical data presents a persistent challenge to data quality in quantitative sociological research, where simpler approaches can lead to biased estimates and incorrect conclusions.
Yaroslav Kostenko, Andrii Gorbachyk
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The existing literature on treatment e¤ects assumes perfect observability of the treatments received by the population of interest. Even in cases of imperfect compliance, it is usually as- sumed that both the assigned and administered treatment are ...
Molinari, Francesca
core
, 2007 We consider a nonparametric and a semiparametric (in presence of covariates) additive hazards rate competing risks model with censoring and failure cause possibly missing completely at random. Estimators of the unknown parameters are proposed in order to
Bordes, Laurent +2 more
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Estimation beyond Missing (Completely) at Random
We study the effects of missingness on the estimation of population parameters. Moving beyond restrictive missing completely at random (MCAR) assumptions, we first formulate a missing data analogue of Huber's arbitrary $\epsilon$-contamination model. For mean estimation with respect to squared Euclidean error loss, we show that the minimax quantiles ...
Ma, Tianyi +4 more
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, 2019 Advances in Neural Information Processing Systems (NeurIPS 2019)
Ma, Wei, Chen, George H.
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Health Data ScienceBackground: Missing data in electronic health records (EHRs) presents significant challenges in medical studies. Many methods have been proposed, but uncertainty exists regarding the current state of missing data addressing methods applied for EHR and ...
Wenhui Ren +5 more
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