SICE: an improved missing data imputation technique
Journal of Big Data, 2020 In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these ...
Shahidul Islam Khan +1 more
doaj +1 more source
Multiple imputation for continuous variables using a Bayesian principal component analysis
, 2015 We propose a multiple imputation method based on principal component analysis (PCA) to deal with incomplete continuous data. To reflect the uncertainty of the parameters from one imputation to the next, we use a Bayesian treatment of the PCA model. Using
Audigier, Vincent +2 more
core +3 more sources
Impact of Asymptomatic Intracranial Hemorrhage on Outcome After Endovascular Stroke Treatment
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Endovascular treatment (EVT) achieves high rates of recanalization in acute large‐vessel occlusion (LVO) stroke, but functional recovery remains heterogeneous. While symptomatic intracranial hemorrhage (sICH) has been well studied, the prognostic impact of asymptomatic intracranial hemorrhage (aICH) after EVT is less certain ...
Shihai Yang +22 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
Multiple Imputation for Multilevel Data with Continuous and Binary Variables [PDF]
, 2017 We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing.
Audigier, Vincent +7 more
core +7 more sources
Five‐Year Disease Progression in Synuclein Seeding Positive Sporadic Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To provide a comprehensive description of disease progression in synuclein seeding assay (SAA) positive sporadic Parkinson Disease participants, using Neuronal Synuclein Disease integrated biological and functional impairment staging framework.
Paulina Gonzalez‐Latapi +19 more
wiley +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
ACCOUNTING FOR MONOTONE ATTRITION IN A POSTPARTUM DEPRESSION CLINICAL TRIAL [PDF]
, 2006 Longitudinal studies in public health, medicine and the social sciences are often complicated by monotone attrition, where a participant drops out before the end of the study and all his/her subsequent measurements are missing.
Roumani, Yazan
core
Fractional Imputation in Survey Sampling: A Comparative Review [PDF]
, 2015 Fractional imputation (FI) is a relatively new method of imputation for handling item nonresponse in survey sampling. In FI, several imputed values with their fractional weights are created for each missing item.
Kim, Jae Kwang +2 more
core +4 more sources