Results 31 to 40 of about 24,713,244 (334)
RIIM:Real-Time Imputation Based on Individual Models [PDF]
Jisuanji kexue, 2022 With the enrichment of data sources,data can be obtained easily but with low quality,resulting that the MVs are ubi-quitous and hard to avoid.Consequently,MV imputation has become one of the classical problems in the field of data quality mana-gement ...
LI Xia, MA Qian, BAI Mei, WANG Xi-te, LI Guan-yu, NING Bo
doaj +1 more source
Modern multiple imputation with functional data [PDF]
Stat, 2021 This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state‐of‐the‐art methods, which face major challenges in the fitting of more complex non‐linear models.
Aniruddha Rajendra Rao, Matthew Reimherr
openaire +3 more sources
Missing data, imputation, and endogeneity [PDF]
Journal of Econometrics, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
McDonough, Ian K., Millimet, Daniel L.
openaire +3 more sources
Fairness in Missing Data Imputation
CoRR, 2021 Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings. To mitigate its impact, many missing value imputation methods have been developed.
Yiliang Zhang, Qi Long
openaire +2 more sources
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
semanticscholar +1 more source
On the regression method of estimation of population mean from incomplete survey data through imputation [PDF]
, 2005 When some observations in the sample data are missing, the application of the regression method is considered for the estimation of population mean with and without the use of imputation.
Shalabh, Toutenburg, Helge
core +2 more sources
Nonparametric Imputation by Data Depth [PDF]
Journal of the American Statistical Association, 2019 We present single imputation method for missing values which borrows the idea of data depth---a measure of centrality defined for an arbitrary point of a space with respect to a probability distribution or data cloud. This consists in iterative maximization of the depth of each observation with missing values, and can be employed with any properly ...
Mozharovskyi, Pavlo +2 more
openaire +4 more sources
Data Imputation with Iterative Graph Reconstruction [PDF]
AAAI Conference on Artificial Intelligence, 2022 Effective data imputation demands rich latent ``structure" discovery capabilities from ``plain" tabular data. Recent advances in graph neural networks-based data imputation solutions show their structure learning potentials by translating tabular data as
J. Zhong, Weiwei Ye, Ning Gui
semanticscholar +1 more source
Water, 2021 Water level data obtained from telemetry stations typically contains large number of outliers. Anomaly detection and a data imputation are necessary steps in a data monitoring system.
Lattawit Kulanuwat +6 more
semanticscholar +1 more source
Data Imputation for Multivariate Time Series Sensor Data With Large Gaps of Missing Data
IEEE Sensors Journal, 2022 Imputation of missing sensor-collected data is often an important step prior to machine learning and statistical data analysis. One particular data imputation challenge is filling large data gaps when the only related data comes from the same sensor ...
Rui Wu +5 more
semanticscholar +1 more source