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Robust doubly protected estimators for quantiles with missing data
(Springer, 2020-09)
Doubly protected methods are widely used for estimating the population mean of an outcome Y from a sample where the response is missing in some individuals. To compensate for the missing responses, a vector X of covariates ...
Bayesian multivariate nonlinear mixed models for censored longitudinal trajectories with non-monotone missing values
(Sringer Heidelberg, 2023)
The analysis of multivariate longitudinal data may often encounter a difficult task, particularly in the presence of censored measurements induced by detection limits and intermittently missing values arising when subjects ...
Asymptotic behavior of robust estimators in partially linear models with missing responses: The effect of estimating the missing probability on the simplified marginal estimators
(Springer, 2011-11)
In this paper, we consider a semiparametric partially linear regression model where missing data occur in the response. We derive the asymptotic behavior of the robust estimators for the regression parameter and of the ...
Bayesian analysis of survival data with missing censoring indicators
(WILEY, 2021)
In some large clinical studies, it may be impractical to perform the physical examination to every subject at his/her last monitoring time in order to diagnose the occurrence of the event of interest. This gives rise to ...
Systematic reviews do not adequately report or address missing outcome data in their analyses: a methodological survey
(Elsevier, 2018-03-02)
Objectives: To describe how systematic review authors report and address categories of participants with potential missing outcome data of trial participants. Study Design and Setting: Methodological survey of systematic ...
Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation
(Pontificia Universidad Católica del PerúPE, 2022)
Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation
(Pontificia Universidad Católica del Perú. Fondo EditorialPE, 2021)
Incremental missing data imputation via modified granular evolving fuzzy modelImputação incremental de dados faltantes via modelo granular fuzzy evolutivo modificado
(Universidade Federal de LavrasPrograma de Pós-Graduação em Engenharia de Sistemas e AutomaçãoUFLAbrasilDepartamento de Engenharia, 2018)
Estimating additive models with missing responses
(Taylor & Francis, 2016-01)
For multivariate regressors, the Nadaraya-Watson regression estimator suffers from the well-known curse of dimensionality. Additive models overcome this drawback. To estimate the additive components, it is usually assumed ...