Results 251 to 260 of about 230,992 (300)

Deep Learning for Multivariate Time Series Imputation: A Survey

International Joint Conference on Artificial Intelligence
Missing values are ubiquitous in multivariate time series (MTS) data, posing significant challenges for accurate analysis and downstream applications.
Jun Wang, Wenjie Du, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, Qingsong Wen   +6 more
semanticscholar   +1 more source

Compatibility in imputation specification

Behavior Research Methods, 2022
Missing data such as data missing at random (MAR) are unavoidable in real data and have the potential to undermine the validity of research results. Multiple imputation is one of the most widely used MAR-based methods in education and behavioral science applications.
Han, Du, Egamaria, Alacam, Stefany, Mena, Brian T, Keller   +3 more
openaire   +2 more sources

Flexible Imputation of Missing Data, 2nd ed.

Journal of the American Statistical Association, 2019
Missing data are frequently encountered in practice. A broader class of missing data is called incomplete data, which includes data with measurement error, multilevel data with latent variables, and potential outcomes in causal inference.
Shu Yang
semanticscholar   +1 more source

TSI-Bench: Benchmarking Time Series Imputation

arXiv.org
Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectively ...
Wenjie Du, Jun Wang, Linglong Qian, Yiyuan Yang, Fanxing Liu, Zepu Wang, Zina M. Ibrahim, Haoxin Liu, Zhiyuan Zhao, Yingjie Zhou, Wenjia Wang, Kaize Ding, Yuxuan Liang, B. Prakash, Qingsong Wen   +14 more
semanticscholar   +1 more source

Multiple Imputation: An Iterative Regression Imputation

2023
Multiple imputation (MI) is a commonly applied method of statistically handling missing data. It involves imputing missing values repeatedly to account for the variability due to imputations. There are different techniques of MI that have proven to be effective and available in many statistical software packages.
Bintou, T., Ismaila, A. A.
openaire   +1 more source

IMPUTATION

Retail and Distribution Management, 1973
From time to time we have had occasion to refer to earnings per share in terms of the new ‘imputation’ system of company taxation. This is a somewhat complex system and we have asked our Financial Correspondent to explain in some detail what is involved.
openaire   +1 more source

CLEMI-Imputation Evaluation

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2018
Missing data is challenging enough without the added complexities posed by a lack of research in evaluating imputation. Not only could we potentially increase the impact and validity of studies from many different sectors (research, public and private), we also believe that by creating evaluation software, more researchers may be willing to use and ...
Anthony Chapman, Wei Pang 0001, George M. Coghill   +2 more
openaire   +1 more source

Robust data imputation

Computational Biology and Chemistry, 2009
Single imputation methods have been wide-discussed topics among researchers in the field of bioinformatics. One major shortcoming of methods proposed until now is the lack of robustness considerations. Like all data, gene expression data can possess outlying values.
vanden Branden, Karlien, Verboven, Sabine   +1 more
openaire   +3 more sources

Imputation

2021
Jae Kwang Kim, Jun Shao
  +4 more sources

Multiple Imputation For Nonresponse In Surveys

, 2016

semanticscholar   +1 more source