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Multiple Imputation and its Application, by James R. Carpenter and Michael G. Kenward, provides an excellent review of multiple imputation (MI) from basic to advanced concepts. MI is a statistical method for analyzing incomplete data. The flexibility of the MI procedure has prompted its use in...
Finally, some real data analyses are performed to compare the before and after imputation results. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. 展开 关键词: accelerometer data missing primary sampling units multiple imputation alternative ...
In this review, application of multiple imputations, as a novel technique for handling missing data in health and epidemiological research, is briefly discussed.Mir Mohammadkhani, MajidHolakouie Naieni, KouroshJournal of School of Public Health & Institute of Public Health Research...
Attribution of metabolomic state and clinical predictors to brain volumes The application of several sets of predictors (metabolomic state, age + sex, multimorbidity score, GRS) was examined in the analysis. The largest variance of brain volumes was explained by age + sex, followed by me...
A fast and accurate algorithm to test for binary phenotypes and its application to PheWAS. Am. J. Hum. Genet. 101, 37–49 (2017). Article CAS PubMed PubMed Central Google Scholar Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale ...
In addition, we further evaluated the biologically interpretable of the scCube-simulated data by comparing the benchmark results of three types of SRT computational methods (spot deconvolution, gene imputation, and spatial domain identification) on the real data and scCube-simulated data. As shown ...
When an emergency event occurs, it is critical to respond in the shortest possible time. Therefore, the rationality and effectiveness of emergency decisions are the key links in emergency management. In this paper, with aims to investigate the problem of emergency alternatives selection, in which ...
To address the issue of missing data, we employed a k-Nearest Neighbors-based imputation method [36, 45] to impute missing values as part of the LR, DT, and RF training pipeline. The imputation model was fitted on the training data and predicted missing values in the testing data. ...
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