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
Multiple Imputation of Missing Values [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2004 Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of “complete-case” analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of ...
P. Royston
openaire +3 more sources
Statistical data preparation: management of missing values and outliers [PDF]
Korean Journal of Anesthesiology, 2017 Missing values and outliers are frequently encountered while collecting data. The presence of missing values reduces the data available to be analyzed, compromising the statistical power of the study, and eventually the reliability of its results.
Sang Kyu Kwak, Jong Hae Kim
doaj +2 more sources
Conformal Prediction with Missing Values [PDF]
International Conference on Machine Learning, 2023 Conformal prediction is a theoretically grounded framework for constructing predictive intervals. We study conformal prediction with missing values in the covariates -- a setting that brings new challenges to uncertainty quantification.
Margaux Zaffran +3 more
semanticscholar +1 more source
Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics. [PDF]
Journal of Proteome Research, 2023 Missing values are a notable challenge when analyzing mass spectrometry-based proteomics data. While the field is still actively debating the best practices, the challenge increased with the emergence of mass spectrometry-based single-cell proteomics and
Christophe Vanderaa, L. Gatto
semanticscholar +1 more source
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
Graph convolutional networks for traffic forecasting with missing values [PDF]
Data mining and knowledge discovery, 2022 Traffic forecasting has attracted widespread attention recently. In reality, traffic data usually contains missing values due to sensor or communication errors.
Jingwei Zuo +3 more
semanticscholar +1 more source
Recurrent Neural Networks for Multivariate Time Series with Missing Values [PDF]
Scientific Reports, 2016 Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
Zhengping Che +4 more
semanticscholar +1 more source
Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods
Inf., 2023 The existence of missing values reduces the amount of knowledge learned by the machine learning models in the training stage thus affecting the classification accuracy negatively. To address this challenge, we introduce the use of Support Vector Machine (
Ashokkumar Palanivinayagam +1 more
semanticscholar +1 more source
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values [PDF]
Transportation Research Part C: Emerging Technologies, 2020 Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RNN-based models in traffic forecasting has not yet been fully exploited in ...
Zhiyong Cui +3 more
semanticscholar +1 more source