Linear mixed models to handle missing at random data in trial‐based economic evaluations
Sign Up to like & getrecommendations! Published in 2022 at "Health Economics"
DOI: 10.1002/hec.4510
Abstract: Abstract Trial‐based cost‐effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may… read more here.
Keywords: trial based; missing random; trial; linear mixed ... See more keywords
Feature screening for ultrahigh-dimensional survival data when failure indicators are missing at random
Sign Up to like & getrecommendations! Published in 2019 at "Statistical Papers"
DOI: 10.1007/s00362-019-01128-5
Abstract: In modern statistical applications, the dimension of covariates can be much larger than the sample size, and extensive research has been done on screening methods which can effectively reduce the dimensionality. However, the existing feature… read more here.
Keywords: missing random; feature screening; failure indicators; indicators missing ... See more keywords
Doubly robust kernel density estimation when group membership is missing at random
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Statistical Planning and Inference"
DOI: 10.1016/j.jspi.2019.09.010
Abstract: Abstract When there are subjects with subpopulation memberships missing, the kernel density estimates of the subpopulations based on the subjects with verified memberships may not be valid unless the missingness of the memberships satisfies the… read more here.
Keywords: missing random; prediction model; kernel density; doubly robust ... See more keywords
CBPS-based estimation for linear models with responses missing at random
Sign Up to like & getrecommendations! Published in 2018 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2017.1371752
Abstract: ABSTRACT In this article, based on the covariate balancing propensity score (CBPS), estimators for the regression coefficients and the population mean are obtained, when the responses of linear models are missing at random. It is… read more here.
Keywords: estimation linear; cbps based; based estimation; missing random ... See more keywords
Dimension-reduced empirical likelihood inference for response mean with data missing at random
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Nonparametric Statistics"
DOI: 10.1080/10485252.2017.1339307
Abstract: ABSTRACT To make efficient inference for mean of a response variable when the data are missing at random and the dimension of covariate is not low, we construct three bias-corrected empirical likelihood (EL) methods in… read more here.
Keywords: dimension; response; dimension reduced; empirical likelihood ... See more keywords
Dimension reduction in estimating equations with covariates missing at random
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Nonparametric Statistics"
DOI: 10.1080/10485252.2018.1438610
Abstract: ABSTRACT To estimate parameters defined by estimating equations with covariates missing at random, we consider three bias-corrected nonparametric approaches based on inverse probability weighting, regression and augmented inverse probability weighting. However, when the dimension of… read more here.
Keywords: dimension; dimension reduction; covariates missing; estimating equations ... See more keywords
Co-Clustering of Ordinal Data via Latent Continuous Random Variables and Not Missing at Random Entries
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Computational and Graphical Statistics"
DOI: 10.1080/10618600.2020.1739533
Abstract: ABSTRACT This article is about the co-clustering of ordinal data. Such data are very common on e-commerce platforms where customers rank the products/services they bought. In more detail, we focus on arrays of ordinal (possibly… read more here.
Keywords: latent continuous; clustering ordinal; ordinal data; model ... See more keywords
Missing Outcome Data in Epidemiologic Studies.
Sign Up to like & getrecommendations! Published in 2022 at "American journal of epidemiology"
DOI: 10.1093/aje/kwac179
Abstract: Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible… read more here.
Keywords: missing random; outcome data; missing data; data epidemiologic ... See more keywords
Maximum likelihood abundance estimation from capture-recapture data when covariates are missing at random.
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DOI: 10.1111/biom.13334
Abstract: In capture-recapture experiments, individual covariates may be subject to missingness, especially when the number of captures is small. When the covariate information is missing at random, the inverse probability weighting method and the multiple imputation… read more here.
Keywords: abundance; capture recapture; missing random;
Regularized approach for data missing not at random
Sign Up to like & getrecommendations! Published in 2019 at "Statistical Methods in Medical Research"
DOI: 10.1177/0962280217717760
Abstract: It is common in longitudinal studies that missing data occur due to subjects’ no response, missed visits, dropout, death or other reasons during the course of study. To perform valid analysis in this setting, data… read more here.
Keywords: approach data; regularized approach; missing random; data missing ... See more keywords
Efficient estimators for expectations in nonlinear parametric regression models with responses missing at random
Sign Up to like & getrecommendations! Published in 2019 at "Electronic Journal of Statistics"
DOI: 10.1214/19-ejs1612
Abstract: Abstract: We consider nonlinear regression models that are solely defined by a parametric model for the regression function. The responses are assumed to be missing at random, with the missingness depending on multiple covariates. We… read more here.
Keywords: efficient estimators; regression; regression models; expectations nonlinear ... See more keywords