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Multiple imputation: dealing with missing data [PDF]

Nephrology Dialysis Transplantation, 2013
In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values ...
Goeij, M.C.M. de, Diepen, M. van, Jager, K.J., Tripepi, G., Zoccali, C., Dekker, F.W.   +5 more
openaire   +6 more sources

RDIS: Random Drop Imputation With Self-Training for Incomplete Time Series Data

IEEE Access, 2023
Time-series data with missing values are a common occurrence in various fields, including healthcare, meteorology, and robotics. The process of imputation aims to fill in the missing values with valid values.
Tae-Min Choi, Ji-Su Kang, Jong-Hwan Kim
doaj   +1 more source

MIAEC: Missing Data Imputation Based on the Evidence Chain

IEEE Access, 2018
Missing or incorrect data caused by improper operations can seriously compromise security investigation. Missing data can not only damage the integrity of the information but also lead to the deviation of the data mining and analysis.
Xiaolong Xu, Weizhi Chong, Shancang Li, Abdullahi Arabo, Jianyu Xiao   +4 more
doaj   +1 more source

Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset

Entropy, 2022
Handling missing values in matrix data is an important step in data analysis. To date, many methods to estimate missing values based on data pattern similarity have been proposed. Most previously proposed methods perform missing value imputation based on
Mayu Tada, Natsumi Suzuki, Yoshifumi Okada   +2 more
doaj   +1 more source

Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation [PDF]

, 2008
Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world
Albrecht, Austin, Baird, Batista, Boehm, Boehm, Breiman, Briand, Briand, Briand, Brockmeier, Cartwright, Cheung, Clark, Feelders, Finnie, Gama, Gray, Holte, Jain, Jeffery, Jun Liu, Jönsson, Kemerer, Khotanzad, Kibler, Kim, Kitchenham, Kohavi, Little, Little, Little, Little, Little, Martin Shepperd, Miranda, Myrtveit, Pickard, Putnam, Qinbao Song, Quinlan, Robins, Rubin, Rubin, Rubin, Rubin, Samson, Selby, Shao, Shepperd, Shepperd, Siedelecki, Song, Song, Srinivasan, Strike, Tabachnick, Tay, Walkerden, Walston, Xiangru Chen   +60 more
core   +1 more source

Informer-WGAN: High Missing Rate Time Series Imputation Based on Adversarial Training and a Self-Attention Mechanism

Algorithms, 2022
Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis.
Yufan Qian, Limei Tian, Baichen Zhai, Shufan Zhang, Rui Wu   +4 more
doaj   +1 more source

Missing Data Imputation with High-Dimensional Data

The American Statistician, 2023
Imputation of missing data in high-dimensional datasets with more variables P than samples N, P≫N, is hampered by the data dimensionality. For multivariate imputation, the covariance matrix is ill conditioned and cannot be properly estimated. For fully conditional imputation, the regression models for imputation cannot include all the variables.
Alberto Brini, Edwin R. van den Heuvel
openaire   +1 more source

Kernel weighted least square approach for imputing missing values of metabolomics data

Scientific Reports, 2021
Mass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing ...
Nishith Kumar, Md. Aminul Hoque, Masahiro Sugimoto   +2 more
doaj   +1 more source

Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms

Mathematics, 2022
A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed.
Tuo Sun, Shihao Zhu, Ruochen Hao, Bo Sun, Jiemin Xie   +4 more
doaj   +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