Results 61 to 70 of about 290,361 (291)
An Empirical Comparison of Multiple Imputation Methods for Categorical Data
, 2016 Multiple imputation is a common approach for dealing with missing values in statistical databases. The imputer fills in missing values with draws from predictive models estimated from the observed data, resulting in multiple, completed versions of the ...
Akande, Olanrewaju +2 more
core +2 more sources
Multiple imputation of multiple multi-item scales when a full imputation model is infeasible [PDF]
, 2016 Background Missing data in a large scale survey presents major challenges. We focus on performing multiple imputation by chained equations when data contain multiple incomplete multi-item scales.
Morris, TP
core +1 more source
Molecular Oncology, EarlyView.Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson +9 more
wiley +1 more source
The ability of different imputation methods for missing values in mental measurement questionnaires
BMC Medical Research Methodology, 2020 Background Incomplete data are of particular important influence in mental measurement questionnaires. Most experts, however, mostly focus on clinical trials and cohort studies and generally pay less attention to this deficiency. We aim is to compare the
Xueying Xu +5 more
doaj +1 more source
Missing Value Imputation for RNA-Sequencing Data Using Statistical Models: A Comparative Study [PDF]
Journal of Statistical Theory and Applications (JSTA), 2016 RNA-seq technology has been widely used as an alternative approach to traditional microarrays in transcript analysis. Sometimes gene expression by sequencing, which generates RNA-seq data set, may have missing read counts.
Taban Baghfalaki +2 more
doaj +1 more source
An Intelligent Missing Data Imputation Techniques: A Review
JOIV: International Journal on Informatics Visualization, 2022 The incomplete dataset is an unescapable problem in data preprocessing that primarily machine learning algorithms could not employ to train the model.
Kimseth Seu, Mi-Sun Kang, HwaMin Lee
doaj +1 more source
Integration of survey data and big observational data for finite population inference using mass imputation [PDF]
, 2018 Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining a probability sample with big observational
Kim, Jae Kwang +2 more
core +3 more sources
Finite sample properties of multiple imputation estimators
, 2004 Finite sample properties of multiple imputation estimators under the linear regression model are studied. The exact bias of the multiple imputation variance estimator is presented. A method of reducing the bias is presented and simulation is used to make
Kim, Jae Kwang
core +1 more source
Multiple imputation methods for bivariate outcomes in cluster randomised trials. [PDF]
, 2016 Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully ...
DiazOrdaz, K +3 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective This study aims to identify both fluid and neuroimaging biomarkers for CSF1R‐RD that can inform the optimal timing of treatment administration to maximize therapeutic benefit, while also providing sensitive quantitative measurements to monitor disease progression.
Tomasz Chmiela +13 more
wiley +1 more source