Article
Biochemical Research Methods
Mengbo Li, Gordon K. Smyth
Summary: Mass spectrometry proteomics in biomedical research suffers from the problem of missing values in peptides. Many analysis strategies have been proposed to distinguish different types of missing values and estimate detection probabilities. A logit-linear function is used to accurately model the detection probability, showing that missing values are related to peptide intensity. A probability model is developed to infer the distribution of unobserved intensities from observed values.
Article
Computer Science, Information Systems
Shahla Faisal, Gerhard Tutz
Summary: The article discusses the use of multiple imputation methods to address missing values in medical research, particularly in high-dimensional data settings. The proposed method based on nearest neighbors successfully imputes missing values and performs well in simulated data comparisons.
INFORMATION SCIENCES
(2021)
Article
Biotechnology & Applied Microbiology
Paul L. L. Auer, Gao Wang, Guangyou Li, Andrew T. T. DeWan, Suzanne M. M. Leal
Summary: This study presents a novel method for integrating imputation uncertainty into statistical association tests. The method has been compared with other approaches and shows good performance in terms of computational efficiency, power, and controlling error rates.
Article
Biology
Lauren J. Beesley, Jeremy M. G. Taylor
Summary: MICE is a popular approach for handling missing data, and we propose a novel strategy to directly incorporate the analysis model by stacking multiple imputations.
Article
Computer Science, Information Systems
Kyoham Shin, Jongmin Han, Seokho Kang
Summary: The study introduces a multiple imputation-based minority oversampling technique named MI-MOTE to address class imbalance and data incompleteness simultaneously. It diversifies minority instances by oversampling them with multiple different imputations, resulting in less data distortion compared to conventional approaches. The proposed method is applied in the data preprocessing phase and has shown effectiveness in handling benchmark datasets with various missing rates.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Manar D. Samad, Sakib Abrar, Norou Diawara
Summary: This paper proposes methods to improve the imputation accuracy of the MICE algorithm by using ensemble learning and deep neural networks. The results of extensive analyses on multiple datasets show that the proposed methods outperform other state-of-the-art imputation algorithms, leading to better imputation accuracy and classification accuracy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Saul Justin Newman, Robert T. Furbank
Summary: The critical shortage of 'big' agronomic data is being addressed by providing a large non-relational database of agronomic trials, linked to intensive management and observational data. This database contains over a million machine-measured phenotypic observations from thousands of field sites across Australia, providing valuable insights for model development and testing in agronomic research.
Article
Computer Science, Artificial Intelligence
Feng Zhao, Yan Lu, Xinning Li, Lina Wang, Yingjie Song, Deming Fan, Caiming Zhang, Xiaobo Chen
Summary: Credit risk assessment is crucial for banks in loan approval and risk management. However, missing credit risk data can significantly reduce the effectiveness of the assessment model. In this paper, a novel method named MGAIN is proposed to accurately predict missing data through subset selection and multiple imputation strategy, improving the accuracy of the imputation model.
APPLIED SOFT COMPUTING
(2022)
Article
Urology & Nephrology
Katrina Blazek, Anita van Zwieten, Valeria Saglimbene, Armando Teixeira-Pinto
Summary: Health data often have missing values, and utilizing multiple imputation techniques can help reduce bias and maintain sample size. Correct specification of the imputation model is crucial for the validity of analyses. Considerations such as missing mechanism, imputation method, and result reporting are important when conducting research with multiply imputed data.
KIDNEY INTERNATIONAL
(2021)
Article
Biochemical Research Methods
Yuan Luo
Summary: Clinical data often have missing entries, posing a challenge to deriving optimal knowledge from the data. The Data Analytics Challenge on Missing data Imputation (DACMI) provides a benchmark dataset for evaluating and advancing imputation techniques for clinical time series. Competitive machine learning and statistical models coupled with carefully engineered features show strong performance in imputation.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Mathematics, Interdisciplinary Applications
X. M. Kavelaars, J. R. van Ginkel, S. van Buuren
Summary: Researchers studied the scenario where imputations were already created before the arrival of the next wave of data. They found that NEST and APPEND have the same validity as RE-IMPUTE for monotone missing data-patterns and perform well when relations within waves are stronger.
MULTIVARIATE BEHAVIORAL RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Han Honggui, Sun Meiting, Wu Xiaolong, Li Fangyu
Summary: This article proposes a double-cycle weighted imputation (DCWI) method to deal with multiple missing patterns in the wastewater treatment process. The method maximizes the utilization of available information to improve imputation accuracy and experimental results show its superiority over comparison methods.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Article
Health Care Sciences & Services
Lauren J. Beesley, Irina Bondarenko, Michael R. Elliot, Allison W. Kurian, Steven J. Katz, Jeremy M. G. Taylor
Summary: This paper describes how to generalize the sequential regression multiple imputation procedure to handle non-random missingness when missingness may depend on other variables. The method reduces bias in the final analysis compared to standard techniques, using approximation strategies involving inclusion of an offset in the imputation model.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2021)
Article
Materials Science, Multidisciplinary
William Steinhardt, P. A. Maksimov, Sachith Dissanayake, Zhenzhong Shi, Nicholas P. Butch, David Graf, Andrey Podlesnyak, Yaohua Liu, Yang Zhao, Guangyong Xu, Jeffrey W. Lynn, Casey Marjerrison, A. L. Chernyshev, Sara Haravifard
Summary: Research suggests a difference in magnetic anisotropy between YbZnGaO4 and YbMgGaO4, leading to the proposal of a new phase diagram dependent on magnetic and exchange parameters, expanding the observable magnetic states in applied field.
NPJ QUANTUM MATERIALS
(2021)
Article
Public, Environmental & Occupational Health
Chinchin Wang, Tyrel Stokes, Russell J. Steele, Niels Wedderkopp, Ian Shrier
Summary: Researchers demonstrated that random hot deck imputation can achieve plausible multiple imputation in longitudinal studies, serving as an alternative method when model-based approaches are infeasible.
CLINICAL EPIDEMIOLOGY
(2022)
