Article
Psychology, Educational
David Goretzko
Summary: The study evaluated the accuracy of different factor retention criteria and missing data methods in determining the number of factors, finding that in most cases, the missing data mechanism had little impact on accuracy, and pairwise deletion performed well.
EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
(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
Computer Science, Artificial Intelligence
C. G. Marcelino, G. M. C. Leite, P. Celes, C. E. Pedreira
Summary: This paper investigates the effects and possible solutions to incomplete databases in regression and provides a systematic view of how missing data may affect regression results by analyzing actual publicly available databases. The results indicate that the impact of missing data can be significant, and the K-Nearest Neighbors method performs better in regression with missing data.
APPLIED ARTIFICIAL INTELLIGENCE
(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
Genetics & Heredity
Richard Howey, Alexander D. Clark, Najib Naamane, Louise N. Reynard, Arthur G. Pratt, Heather J. Cordell
Summary: Bayesian networks can be improved by addressing missing data with nearest neighbour imputation and upweighting certain network edges using a pseudo-Bayesian approach. These improvements result in higher recall and precision of directed edges in the final network, especially useful in biological scenarios with complex variable relationships.
Article
Energy & Fuels
Dongyeon Jeong, Chiwoo Park, Young Myoung Ko
Summary: The study introduces a mixture factor analysis method for estimating missing values in building electric load data. Due to quality issues in building electric load data, a novel data imputation model is proposed to represent patterns and their cyclic rotations, providing better handling of missing data problems and improving efficiency and accuracy in model selection.
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
Mathematics
Fangfang Li, Hui Sun, Yu Gu, Ge Yu
Summary: This paper proposes a noise-aware missing data multiple imputation algorithm NPMI for static data. Different multiple imputation models are proposed according to the missing mechanism of data. The method to determine the imputation order of multivariablesmissing is given. Experiments on real and synthetic datasets verify the accuracy and efficiency of the proposed algorithm.
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
Multidisciplinary Sciences
Hannah Voss, Simon Schlumbohm, Philip Barwikowski, Marcus Wurlitzer, Matthias Dottermusch, Philipp Neumann, Hartmut Schlueter, Julia E. Neumann, Christoph Krisp
Summary: HarmonizR is an efficient tool for missing data tolerant experimental variance reduction, which does not require data imputation and can be easily adjusted for individual dataset properties and user preferences. It demonstrated successful data harmonization for different tissue preservation techniques, LC-MS/MS instrumentation setups, and quantification approaches, and outperformed data imputation methods in detecting significant proteins.
NATURE COMMUNICATIONS
(2022)
Article
Mathematical & Computational Biology
Jeong Hoon Jang, Amita K. Manatunga, Changgee Chang, Qi Long
Summary: This study introduces a Bayesian multiple imputation approach for bivariate functional data with missing components, demonstrating superior performance through simulation studies under various designs and missingness rates. The proposed method is successfully applied to impute missing post-furosemide renogram curves in the motivating renal study, providing more refined insights into renal obstruction mechanisms.
STATISTICS IN MEDICINE
(2021)
Article
Health Care Sciences & Services
Martijn W. Heymans, Jos W. R. Twisk
Summary: Proper handling of missing data is crucial, and consideration should be given to the mechanism of missing data. Multiple imputations are highly recommended for estimating missing values. It is important to prevent missing data rather than treating them.
JOURNAL OF CLINICAL EPIDEMIOLOGY
(2022)
Article
Psychology, Mathematical
Aaron J-M Lim, Mike W-L Cheung
Summary: This study examined four approaches to handling missing data in CFA models with a mix of continuous and ordinal variables, finding that FIML produced unbiased estimations in most conditions. When sample size was large, fully conditional specification combined with WLSMV was the second best option.
BEHAVIOR RESEARCH METHODS
(2022)
Article
Ecology
Thomas F. Johnson, Nick J. B. Isaac, Agustin Paviolo, Manuela Gonzalez-Suarez
Summary: The study evaluated the performance of approaches for handling missing values in biased datasets and found that imputation can effectively handle missing data in some conditions but is not always the best solution. None of the tested methods could effectively deal with severe biases, highlighting the importance of rigorous data checking and proposing variables to assist researchers in detecting and minimizing errors in incomplete datasets.
GLOBAL ECOLOGY AND BIOGEOGRAPHY
(2021)
Review
Biochemical Research Methods
Sandra Taylor, Matthew Ponzini, Machelle Wilson, Kyoungmi Kim
Summary: Missing values are common in high-throughput mass spectrometry data. Two strategies are available to address missing values: imputation and imputation-free methods. This study reviews the impact of sample size and percentage of missing values on statistical inference, comparing the performance of different methods.
BRIEFINGS IN BIOINFORMATICS
(2022)