Results 1 to 10 of about 1,170,104 (261)
Optimal clustering with missing values [PDF]
BMC Bioinformatics, 2019 Background Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements.
Shahin Boluki +3 more
doaj +4 more sources
A note on handling conditional missing values [PDF]
Global EpidemiologyIn medical research, some variables are conditionally defined on some levels of another variable, leading to conditional missing data. Imputation of this type of structural missing data is needed given inefficiency of listwise deletion inherent in ...
Mohammad Ali Mansournia +2 more
doaj +2 more sources
Sample Entropy Computation on Signals with Missing Values [PDF]
EntropySample entropy embeds time series into m-dimensional spaces and estimates entropy based on the distances between points in these spaces. However, when samples can be considered as missing or invalid, defining distance in the embedding space becomes ...
George Manis +2 more
doaj +2 more sources
Assessing the Performance of a Long Short-Term Memory Algorithm in the Dataset with Missing Values [PDF]
대한환경공학회지, 2022 This study was conducted to assess the performance of a long short-term memory algorithm (LSTM), which was suitable for time series prediction, in the multivariate dataset with missing values.
Hyun-Geoun Park +4 more
doaj +1 more source
Missing Values in Panel Data Unit Root Tests
Econometrics, 2022 Missing data or missing values are a common phenomenon in applied panel data research and of great interest for panel data unit root testing. The standard approach in the literature is to balance the panel by removing units and/or trimming a common time ...
Yiannis Karavias +2 more
doaj +1 more source
Mathematics, 2023 Navigating the intricate world of data analytics, one method has emerged as a key tool in confronting missing data: multiple imputation. Its strength is further fortified by its powerful variant, robust imputation, which enhances the precision and ...
Matthias Templ
doaj +1 more source
Imputation with the R Package VIM
Journal of Statistical Software, 2016 The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to explore and analyze the structure of missing values in data using visualization methods, to impute these missing values with the built-in imputation methods and to verify the ...
Alexander Kowarik, Matthias Templ
doaj +1 more source
Large‐scale data visualization with missing values
Technological and Economic Development of Economy, 2006 Visualization of large‐scale data inherently requires dimensionality reduction to 1D, 2D, or 3D space. Autoassociative neural networks with a bottleneck layer are commonly used as a nonlinear dimensionality reduction technique.
Sergiy Popov
doaj +1 more source
Metabolites, 2020 To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite’s true effect size may lead to ...
Christopher E. Gillies +7 more
doaj +1 more source
Imputing missing values using cumulative linear regression
CAAI Transactions on Intelligence Technology, 2019 The concept of missing data is important to apply statistical methods on the dataset. Statisticians and researchers may end up to an inaccurate illation about the data if the missing data are not handled properly.
Samih M. Mostafa
doaj +1 more source