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Thematische Bibliographien / Data missingness / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Data missingness“

Autor: Grafiati

Veröffentlicht am 24. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Shamihah, Muhammad Ghazali, Shaadan Norshahida, and Idrus Zainura. "Missing data exploration in air quality data set using R-package data visualisation tools." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 755–63. https://doi.org/10.11591/eei.v9i2.2088.

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Missing values often occur in many data sets of various research areas. This has been recognized as data quality problem because missing values could affect the performance of analysis results. To overcome the problem, the incomplete data set needs to be treated using imputation method. Thus, exploring missing values pattern must be conducted beforehand to determine a suitable method. This paper discusses on the application of data visualisation as a smart technique for missing data exploration aiming to increase understanding on missing data behaviour which include missing data mechanism (MCA

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Ghazali, Shamihah Muhammad, Norshahida Shaadan, and Zainura Idrus. "Missing data exploration in air quality data set using R-package data visualisation tools." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 755–63. http://dx.doi.org/10.11591/eei.v9i2.2088.

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Missing values often occur in many data sets of various research areas. This has been recognized as data quality problem because missing values could affect the performance of analysis results. To overcome the problem, the incomplete data set need to be treated or replaced using imputation method. Thus, exploring missing values pattern must be conducted beforehand to determine a suitable method. This paper discusses on the application of data visualisation as a smart technique for missing data exploration aiming to increase understanding on missing data behaviour which include missing data mec

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Beesley, Lauren J., Irina Bondarenko, Michael R. Elliot, Allison W. Kurian, Steven J. Katz, and Jeremy MG Taylor. "Multiple imputation with missing data indicators." Statistical Methods in Medical Research 30, no. 12 (2021): 2685–700. http://dx.doi.org/10.1177/09622802211047346.

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Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can ge

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Beesley, Lauren J., Irina Bondarenko, Michael R. Elliot, Allison W. Kurian, Steven J. Katz, and Jeremy MG Taylor. "Multiple imputation with missing data indicators." Statistical Methods in Medical Research 30, no. 12 (2021): 2685–700. http://dx.doi.org/10.1177/09622802211047346.

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Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can ge

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ZHANG, WEN, YE YANG, and QING WANG. "A COMPARATIVE STUDY OF ABSENT FEATURES AND UNOBSERVED VALUES IN SOFTWARE EFFORT DATA." International Journal of Software Engineering and Knowledge Engineering 22, no. 02 (2012): 185–202. http://dx.doi.org/10.1142/s0218194012400025.

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Software effort data contains a large amount of missing values of project attributes. The problem of absent features, which occurred recently in machine learning, is often neglected by researchers of software engineering when handling the missingness in software effort data. In essence, absent features (structural missingness) and unobserved values (unstructured missingness) are different cases of missingness although their appearance in the data set are the same. This paper attempts to clarify the root cause of missingness of software effort data. When regarding missingness as absent features

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Wu, Ting Ting, Louisa H. Smith, Lisette M. Vernooij, Emi Patel, and John W. Devlin. "Data Missingness Reporting and Use of Methods to Address It in Critical Care Cohort Studies." Critical Care Explorations 5, no. 11 (2023): e1005. http://dx.doi.org/10.1097/cce.0000000000001005.

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IMPORTANCE: Failure to recognize and address data missingness in cohort studies may lead to biased results. Although Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines advocate data missingness reporting, the degree to which missingness is reported and addressed in the critical care literature remains unclear. OBJECTIVES: To review published ICU cohort studies to characterize data missingness reporting and the use of methods to address it. DESIGN, SETTING, AND PARTICIPANTS: We searched the 2022 table of contents of 29 critical care/critical care subspecia

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De Raadt, Alexandra, Matthijs J. Warrens, Roel J. Bosker, and Henk A. L. Kiers. "Kappa Coefficients for Missing Data." Educational and Psychological Measurement 79, no. 3 (2019): 558–76. http://dx.doi.org/10.1177/0013164418823249.

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Cohen’s kappa coefficient is commonly used for assessing agreement between classifications of two raters on a nominal scale. Three variants of Cohen’s kappa that can handle missing data are presented. Data are considered missing if one or both ratings of a unit are missing. We study how well the variants estimate the kappa value for complete data under two missing data mechanisms—namely, missingness completely at random and a form of missingness not at random. The kappa coefficient considered in Gwet ( Handbook of Inter-rater Reliability, 4th ed.) and the kappa coefficient based on listwise de

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Arioli, Angelica, Arianna Dagliati, Bethany Geary, et al. "OptiMissP: A dashboard to assess missingness in proteomic data-independent acquisition mass spectrometry." PLOS ONE 16, no. 4 (2021): e0249771. http://dx.doi.org/10.1371/journal.pone.0249771.

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Background Missing values are a key issue in the statistical analysis of proteomic data. Defining the strategy to address missing values is a complex task in each study, potentially affecting the quality of statistical analyses. Results We have developed OptiMissP, a dashboard to visually and qualitatively evaluate missingness and guide decision making in the handling of missing values in proteomics studies that use data-independent acquisition mass spectrometry. It provides a set of visual tools to retrieve information about missingness through protein densities and topology-based approaches,

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Babcock, Ben, Peter E. L. Marks, Yvonne H. M. van den Berg, and Antonius H. N. Cillessen. "Implications of systematic nominator missingness for peer nomination data." International Journal of Behavioral Development 42, no. 1 (2016): 148–54. http://dx.doi.org/10.1177/0165025416664431.

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Missing data are a persistent problem in psychological research. Peer nomination data present a unique missing data problem, because a nominator’s nonparticipation results in missing data for other individuals in the study. This study examined the range of effects of systematic nonparticipation on the correlations between peer nomination data when nominators with various levels of popularity and social preference are missing. Results showed that, compared to completely random nominator missingness, systematic missingness of raters based on popularity had a significant impact on the correlation

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Xie, Hui. "Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons." Computational Statistics & Data Analysis 56, no. 5 (2012): 1287–300. http://dx.doi.org/10.1016/j.csda.2010.11.021.

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Spineli, Loukia M., Chrysostomos Kalyvas, and Katerina Papadimitropoulou. "Continuous(ly) missing outcome data in network meta-analysis: A one-stage pattern-mixture model approach." Statistical Methods in Medical Research 30, no. 4 (2021): 958–75. http://dx.doi.org/10.1177/0962280220983544.

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Appropriate handling of aggregate missing outcome data is necessary to minimise bias in the conclusions of systematic reviews. The two-stage pattern-mixture model has been already proposed to address aggregate missing continuous outcome data. While this approach is more proper compared with the exclusion of missing continuous outcome data and simple imputation methods, it does not offer flexible modelling of missing continuous outcome data to investigate their implications on the conclusions thoroughly. Therefore, we propose a one-stage pattern-mixture model approach under the Bayesian framewo

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Qiao, Jie, Zhengming Chen, Jianhua Yu, Ruichu Cai, and Zhifeng Hao. "Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 20516–23. http://dx.doi.org/10.1609/aaai.v38i18.30036.

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Missing data are an unavoidable complication frequently encountered in many causal discovery tasks. When a missing process depends on the missing values themselves (known as self-masking missingness), the recovery of the joint distribution becomes unattainable, and detecting the presence of such self-masking missingness remains a perplexing challenge. Consequently, due to the inability to reconstruct the original distribution and to discern the underlying missingness mechanism, simply applying existing causal discovery methods would lead to wrong conclusions. In this work, we found that the re

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Mitra, Robin, Sarah F. McGough, Tapabrata Chakraborti, et al. "Learning from data with structured missingness." Nature Machine Intelligence 5, no. 1 (2023): 13–23. http://dx.doi.org/10.1038/s42256-022-00596-z.

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McGurk, Kathryn A., Arianna Dagliati, Davide Chiasserini, et al. "The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination." Bioinformatics 36, no. 7 (2019): 2217–23. http://dx.doi.org/10.1093/bioinformatics/btz898.

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Abstract Motivation Data-independent acquisition mass spectrometry allows for comprehensive peptide detection and relative quantification than standard data-dependent approaches. While less prone to missing values, these still exist. Current approaches for handling the so-called missingness have challenges. We hypothesized that non-random missingness is a useful biological measure and demonstrate the importance of analysing missingness for proteomic discovery within a longitudinal study of disease activity. Results The magnitude of missingness did not correlate with mean peptide concentration.

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Elleman, Lorien G., Sarah K. McDougald, David M. Condon, and William Revelle. "That Takes the BISCUIT." European Journal of Psychological Assessment 36, no. 6 (2020): 948–58. http://dx.doi.org/10.1027/1015-5759/a000590.

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Abstract. The predictive accuracy of personality-criterion regression models may be improved with statistical learning (SL) techniques. This study introduced a novel SL technique, BISCUIT (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent). The predictive accuracy and parsimony of BISCUIT were compared with three established SL techniques (the lasso, elastic net, and random forest) and regression using two sets of scales, for five criteria, across five levels of data missingness. BISCUIT’s predictive accuracy was competitive with other SL techniques at highe

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Rhemtulla, Mijke, Fan Jia, Wei Wu, and Todd D. Little. "Planned missing designs to optimize the efficiency of latent growth parameter estimates." International Journal of Behavioral Development 38, no. 5 (2014): 423–34. http://dx.doi.org/10.1177/0165025413514324.

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We examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a three-form design at each wave such that 25% of data are missing. The second is wave-level planned missingness such that each participant is missing up to two waves of data. The third combines both forms of missingness. We find that three-form missingness results in high convergence rate

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Fernstad, Sara Johansson. "To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization." Information Visualization 18, no. 2 (2018): 230–50. http://dx.doi.org/10.1177/1473871618785387.

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While missing data is a commonly occurring issue in many domains, it is a topic that has been greatly overlooked by visualization scientists. Missing data values reduce the reliability of analysis results. A range of methods exist to replace the missing values with estimated values, but their appropriateness often depend on the patterns of missingness. Increased understanding of the missingness patterns and the distribution of missing values in data may greatly improve reliability, as well as provide valuable insight into potential problems in data gathering and analyses processes, and better

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Goel, Naman, Alfonso Amayuelas, Amit Deshpande, and Amit Sharma. "The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 7564–73. http://dx.doi.org/10.1609/aaai.v35i9.16926.

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Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This missingness, if ignored, nullifies any fairness guarantee of the training procedure when the model is deployed. Using causal graphs, we characterize the missingness mechanisms in different real-world scenarios. We show conditions under which various distributions, used in popular fairness algorithms, can or can not be recovered from the trai

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Forna, Alpha, Ilaria Dorigatti, Pierre Nouvellet, and Christl A. Donnelly. "Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study." PLOS ONE 16, no. 9 (2021): e0257005. http://dx.doi.org/10.1371/journal.pone.0257005.

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Background Machine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous. Methods Using simulated data, we use a ML algorithmic framework to evaluate data imputation performance and the resulting case fatality ratio (CFR) estimates, focusing on the scale and type of data missingness (i.e., missing completely at random—MCAR, missing at random—MAR, or missing not at random—MNAR). Results Across ML methods, dataset sizes and

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Ribeiro, Silvana Mara, and Cristiano Leite Castro. "Missing Data in Time Series: A Review of Imputation Methods and Case Study." Learning and Nonlinear Models 20, no. 1 (2022): 31–46. http://dx.doi.org/10.21528/lnlm-vol20-no1-art3.

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Dealing with missingness in time series data is a very important, but oftentimes overlooked, step in data analysis. In this paper, the nature of time series data and missingness mechanisms are described to help identify which imputation method should be used to impute missing data, along with a review of imputation methods and how they work. Recommended methods from literature are used to impute synthetic data of different nature and the results are discussed. In addition, a case study concerning the prediction (classification) of US market instability (BEAR or BULL) using a data set with mixe

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St-Louis, Etienne, Daniel Roizblatt, Dan L. Deckelbaum, Robert Baird, César V. Millán, and Alicia Ebensperger. "Identifying Pediatric Trauma Data Gaps at a Large Urban Trauma Referral Center in Santiago, Chile." Panamerican Journal of Trauma, Critical Care & Emergency Surgery 6, no. 3 (2017): 169–76. http://dx.doi.org/10.5005/jp-journals-10030-1188.

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ABSTRACT Background Trauma registries contribute to improving trauma care, but their impact is highly dependent on the quality of the data. A simplified point of care pediatric trauma registry (PTR) was developed at the Centre for Global Surgery from the McGill University Health Centre (MUHC) for implementation in Low-middle income countries (LMICs). Pilot deployment was launched at a large urban trauma center in May 2016 in Santiago, Chile. Prior to deployment, we sought to identify missing data in existing trauma records in order to optimize PTR practicality and user benefit. Materials and m

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Yildiz, Mustafa, Vicki Winstead, and Carolyn Pickering. "THE ROLE OF MISSINGNESS IN DAILY DIARY DATA." Innovation in Aging 7, Supplement_1 (2023): 604. http://dx.doi.org/10.1093/geroni/igad104.1974.

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Abstract Objections are often raised to using intensive daily diary research methods with family caregivers of persons with dementia. These objections include the daily lack of time, stress associated with dementia caregiving, in particular, and depression and anxiety associated with the demands of the caregiving process. It is argued that these factors may deter caregivers from consistent compliance and lead to bias in the data. The aim of this paper is to describe and predict missingness in daily diaries using the data from three micro-longitudinal studies that include 10,680 days of diaries

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Sadinle, Mauricio, and Jerome P. Reiter. "Sequentially additive nonignorable missing data modelling using auxiliary marginal information." Biometrika 106, no. 4 (2019): 889–911. http://dx.doi.org/10.1093/biomet/asz054.

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Summary We study a class of missingness mechanisms, referred to as sequentially additive nonignorable, for modelling multivariate data with item nonresponse. These mechanisms explicitly allow the probability of nonresponse for each variable to depend on the value of that variable, thereby representing nonignorable missingness mechanisms. These missing data models are identified by making use of auxiliary information on marginal distributions, such as marginal probabilities for multivariate categorical variables or moments for numeric variables. We prove identification results and illustrate th

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Kim, Jungkyu, Kibok Lee, and Taeyoung Park. "To Predict or Not to Predict? Proportionally Masked Autoencoders for Tabular Data Imputation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17886–94. https://doi.org/10.1609/aaai.v39i17.33967.

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Masked autoencoders (MAEs) have recently demonstrated effectiveness in tabular data imputation. However, due to the inherent heterogeneity of tabular data, the uniform random masking strategy commonly used in MAEs can disrupt the distribution of missingness, leading to suboptimal performance. To address this, we propose a proportional masking strategy for MAEs. Specifically, we first compute the statistics of missingness based on the observed proportions in the dataset, and then generate masks that align with these statistics, ensuring that the distribution of missingness is preserved after ma

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Lu, Zhenqiu, and Zhiyong Zhang. "Bayesian Approach to Non-ignorable Missingness in Latent Growth Models." Journal of Behavioral Data Science 1, no. 2 (2021): 1–30. http://dx.doi.org/10.35566/jbds/v1n2/p1.

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Latent growth curve models (LGCMs) are becoming increasingly important among growth models because they can effectively capture individuals' latent growth trajectories and also explain the factors that influence such growth by analyzing the repeatedly measured manifest variables. However, with the increase in complexity of LGCMs, there is an increase in issues on model estimation. This research proposes a Bayesian approach to LGCMs to address the perennial problem of almost all longitudinal research, namely, missing data. First, different missingness models are formulated. We focus on non-igno

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Plancade, Sandra, Magali Berland, Mélisande Blein-Nicolas, Olivier Langella, Ariane Bassignani, and Catherine Juste. "A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation." PeerJ 10 (June 24, 2022): e13525. http://dx.doi.org/10.7717/peerj.13525.

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One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely “at random” or “not at random”. To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach

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Mainzer, Rheanna, Margarita Moreno-Betancur, Cattram Nguyen, Julie Simpson, John Carlin, and Katherine Lee. "Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review." BMJ Open 13, no. 2 (2023): e065576. http://dx.doi.org/10.1136/bmjopen-2022-065576.

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IntroductionObservational studies in health-related research often aim to answer causal questions. Missing data are common in these studies and often occur in multiple variables, such as the exposure, outcome and/or variables used to control for confounding. The standard classification of missing data as missing completely at random, missing at random (MAR) or missing not at random does not allow for a clear assessment of missingness assumptions when missingness arises in more than one variable. This presents challenges for selecting an analytic approach and determining when a sensitivity anal

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Zamanian, Alireza, Henrik von Kleist, Octavia-Andreea Ciora, Marta Piperno, Gino Lancho, and Narges Ahmidi. "Analysis of Missingness Scenarios for Observational Health Data." Journal of Personalized Medicine 14, no. 5 (2024): 514. http://dx.doi.org/10.3390/jpm14050514.

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Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with

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Doggett, Amanda, Ashok Chaurasia, Jean-Philippe Chaput, and Scott T. Leatherdale. "Using classification and regression trees to model missingness in youth BMI, height and body mass data." Health Promotion and Chronic Disease Prevention in Canada 43, no. 5 (2023): 231–42. http://dx.doi.org/10.24095/hpcdp.43.5.03.

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Introduction Research suggests that there is often a high degree of missingness in youth body mass index (BMI) data derived from self-reported measures, which may have a large effect on research findings. The first step in handling missing data is to examine the levels and patterns of missingness. However, previous studies examiningyouth BMI missingness used logistic regression, which is limited in its ability to discern subgroups or identify a hierarchy of importance for variables, aspects that may go a long way in helping understand missing data patterns. Methods This study used sex-stratifi

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Imai, Takumi. "Methodology of Semiparametric Estimation for Data with Missingness." Japanese Journal of Applied Statistics 46, no. 2 (2017): 87–106. http://dx.doi.org/10.5023/jappstat.46.87.

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Molenberghs, Geert, Els J. T. Goetghebeur, Stuart R. Lipsitz, and Michael G. Kenward. "Nonrandom Missingness in Categorical Data: Strengths and Limitations." American Statistician 53, no. 2 (1999): 110. http://dx.doi.org/10.2307/2685728.

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Cho Paik, Myunghee. "Nonignorable Missingness in Matched Case-Control Data Analyses." Biometrics 60, no. 2 (2004): 306–14. http://dx.doi.org/10.1111/j.0006-341x.2004.00174.x.

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Molenberghs, Geert, Els J. T. Goetghebeur, Stuart R. Lipsitz, and Michael G. Kenward. "Nonrandom Missingness in Categorical Data: Strengths and Limitations." American Statistician 53, no. 2 (1999): 110–18. http://dx.doi.org/10.1080/00031305.1999.10474442.

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Derks, Eske M., Conor V. Dolan, and Dorret I. Boomsma. "Statistical Power to Detect Genetic and Environmental Influences in the Presence of Data Missing at Random." Twin Research and Human Genetics 10, no. 1 (2007): 159–67. http://dx.doi.org/10.1375/twin.10.1.159.

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AbstractWe study the situation in which a cheap measure (X) is observed in a large, representative twin sample, and a more expensive measure (Y) is observed in a selected subsample. The aim of this study is to investigate the optimal selection design in terms of the statistical power to detect genetic and environmental influences on the variance of Y and on the covariance of X and Y. Data were simulated for 4000 dizygotic and 2000 monozygotic twins. Missingness (87% vs. 97%) was then introduced in accordance with 7 selection designs: (i) concordant low + individual high design; (ii) extreme co

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Yu, Yue, Emily J. Smith, and Carter T. Butts. "Retrospective Network Imputation from Life History Data: The Impact of Designs." Sociological Methodology 50, no. 1 (2020): 131–67. http://dx.doi.org/10.1177/0081175020905624.

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Retrospective life history designs are among the few practical approaches for collecting longitudinal network information from large populations, particularly in the context of relationships like sexual partnerships that cannot be measured via digital traces or documentary evidence. While all such designs afford the ability to “peer into the past” vis-à-vis the point of data collection, little is known about the impact of the specific design parameters on the time horizon over which such information is useful. In this article, we investigate the effect of two different survey designs on retros

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van, Oudenhoven Floor M., Sophie H. N. Swinkels, Hilkka Soininen, et al. "A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer's disease." Alzheimer's Research & Therapy 13, no. 1 (2021): 63. https://doi.org/10.1186/s13195-021-00801-y.

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<strong>Background: </strong>Missing data can complicate the interpretability of a clinical trial, especially if the proportion is substantial and if there are different, potentially outcome-dependent causes.<strong>Methods: </strong>We aimed to obtain unbiased estimates, in the presence of a high level of missing data, for the intervention effects in a prodromal Alzheimer's disease trial: the LipiDiDiet study. We used a competing risk joint model that can simultaneously model each patient's longitudinal outcome trajectory in combination with the timing and type of missingness.<strong>Results:

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Chaimani, Anna, Dimitris Mavridis, Georgia Salanti, Julian P. T. Higgins, and Ian R. White. "Allowing for Informative Missingness in Aggregate Data Meta-Analysis with Continuous or Binary Outcomes: Extensions to Metamiss." Stata Journal: Promoting communications on statistics and Stata 18, no. 3 (2018): 716–40. http://dx.doi.org/10.1177/1536867x1801800310.

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Missing outcome data can invalidate the results of randomized trials and their meta-analysis. However, addressing missing data is often a challenging issue because it requires untestable assumptions. The impact of missing outcome data on the meta-analysis summary effect can be explored by assuming a relationship between the outcome in the observed and the missing participants via an informative missingness parameter. The informative missingness parameters cannot be estimated from the observed data, but they can be specified, with associated uncertainty, using evidence external to the meta-anal

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Sanju, Sanju, and Vinay Kumar. "Analysis of Incomplete Data Under Different Missingness Mechanism using Imputation Methods for Wheat Genotypes." Current Agriculture Research Journal 11, no. 3 (2024): 1050–56. http://dx.doi.org/10.12944/carj.11.3.33.

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Missing values is a persistent problem in analysis of agriculture data. To improve the quality of the data in the agriculture study, imputation has drawn a lot of research interest. Non-missing data was removed with varying frequency from the genotypic data of the wheat crop by different missingness mechanism. Imputation methods namely last observation carried forward, mean, regression and KNN are applied to these data sets and compared their parameter with the parameter of original data. The performances of imputation methods are also evaluated by root mean square error for solving missing va

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Zhou, Sherry, and Anne Corinne Huggins-Manley. "The Performance of the Semigeneralized Partial Credit Model for Handling Item-Level Missingness." Educational and Psychological Measurement 80, no. 6 (2020): 1196–215. http://dx.doi.org/10.1177/0013164420918392.

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The semi-generalized partial credit model (Semi-GPCM) has been proposed as a unidimensional modeling method for handling not applicable scale responses and neutral scale responses, and it has been suggested that the model may be of use in handling missing data in scale items. The purpose of this study is to evaluate the ability of the unidimensional Semi-GPCM to aid in the recovery of person parameters from item response data in the presence of item-level missingness, and to compare the performance of the model with two other proposed methods for handling such missingness: a multidimensional m

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Jabbar, Zain, and Peter Washington. "The Effect of Data Missingness on Machine Learning Predictions of Uncontrolled Diabetes Using All of Us Data." BioMedInformatics 4, no. 1 (2024): 780–95. http://dx.doi.org/10.3390/biomedinformatics4010043.

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Electronic Health Records (EHR) provide a vast amount of patient data that are relevant to predicting clinical outcomes. The inherent presence of missing values poses challenges to building performant machine learning models. This paper aims to investigate the effect of various imputation methods on the National Institutes of Health’s All of Us dataset, a dataset containing a high degree of data missingness. We apply several imputation techniques such as mean substitution, constant filling, and multiple imputation on the same dataset for the task of diabetes prediction. We find that imputing v

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Alade, Oyekale Abel, Ali Selamat, and Roselina Sallehuddin. "The Effects of Missing Data Characteristics on the Choice of Imputation Techniques." Vietnam Journal of Computer Science 07, no. 02 (2020): 161–77. http://dx.doi.org/10.1142/s2196888820500098.

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One major characteristic of data is completeness. Missing data is a significant problem in medical datasets. It leads to incorrect classification of patients and is dangerous to the health management of patients. Many factors lead to the missingness of values in databases in medical datasets. In this paper, we propose the need to examine the causes of missing data in a medical dataset to ensure that the right imputation method is used in solving the problem. The mechanism of missingness in datasets was studied to know the missing pattern of datasets and determine a suitable imputation techniqu

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Fang, Zhou, Tianzhou Ma, Gong Tang, et al. "Bayesian integrative model for multi-omics data with missingness." Bioinformatics 34, no. 22 (2018): 3801–8. http://dx.doi.org/10.1093/bioinformatics/bty775.

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Khoshgoftaar, Taghi M., and Jason Van Hulse. "Imputation techniques for multivariate missingness in software measurement data." Software Quality Journal 16, no. 4 (2008): 563–600. http://dx.doi.org/10.1007/s11219-008-9054-7.

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McNeish, Daniel. "Missing data methods for arbitrary missingness with small samples." Journal of Applied Statistics 44, no. 1 (2016): 24–39. http://dx.doi.org/10.1080/02664763.2016.1158246.

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Park, Soomin, Mari Palta, Jun Shao, and Lei Shen. "Bias adjustment in analysing longitudinal data with informative missingness." Statistics in Medicine 21, no. 2 (2001): 277–91. http://dx.doi.org/10.1002/sim.992.

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Franks, Alexander M., Edoardo M. Airoldi, and Donald B. Rubin. "Nonstandard conditionally specified models for nonignorable missing data." Proceedings of the National Academy of Sciences 117, no. 32 (2020): 19045–53. http://dx.doi.org/10.1073/pnas.1815563117.

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Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this formulation, which traces back to a representation of the joint distribution of the data and missingness mechanism, apparently first proposed by J. W. Tukey, the modeling assumptions about the distributions are either assessable or are designed to allow relatively easy incorporation of substantive know

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Zeng, Zhixuan, Yang Liu, Shuo Yao, et al. "Neural networks based on attention architecture are robust to data missingness for early predicting hospital mortality in intensive care unit patients." DIGITAL HEALTH 9 (January 2023): 205520762311714. http://dx.doi.org/10.1177/20552076231171482.

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Background Although the machine learning model developed on electronic health records has become a promising method for early predicting hospital mortality, few studies focus on the approaches for handling missing data in electronic health records and evaluate model robustness to data missingness. This study proposes an attention architecture that shows excellent predictive performance and is robust to data missingness. Methods Two public intensive care unit databases were used for model training and external validation, respectively. Three neural networks (masked attention model, attention mo

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Ruiz, Rizzo Adriana Lucía, Meléndez Mario Eduardo Archila, and Veloza José John Fredy González. "Predicting the probability of finding missing older adults based on machine learning." Journal of Computational Social Science 5, no. 2 (2022): 1303–21. https://doi.org/10.1007/s42001-022-00171-x.

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<strong>Abstract</strong> Person missingness is an enigmatic and frequent phenomenon that can bring about negative consequences for the missing person, their family, and society in general. Age-related cognitive changes and a higher vulnerability to dementia can increase the propensity of older adults to go missing. Thus, it is necessary to better understand the phenomenon of missingness in older adults. The present study sought to identify individual and environmental factors that might predict whether an older adult reported missing will be found. Supervised machine learning models were used

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Blozis, Shelley A. "Bayesian pattern-mixture models for dropout and intermittently missing data in longitudinal data analysis." Behavior Research Methods, May 23, 2023. http://dx.doi.org/10.3758/s13428-023-02128-y.

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AbstractValid inference can be drawn from a random-effects model for repeated measures that are incomplete if whether the data are missing or not, known as missingness, is independent of the missing data. Data that are missing completely at random or missing at random are two data types for which missingness is ignorable. Given ignorable missingness, statistical inference can proceed without addressing the source of the missing data in the model. If the missingness is not ignorable, however, recommendations are to fit multiple models that represent different plausible explanations of the missi

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Zhang, Jiwei, Jing Lu, and Zhaoyuan Zhang. "Modeling Missing Response Data in Item Response Theory: Addressing Missing Not at Random Mechanism with Monotone Missing Characteristics." Journal of Educational Measurement, February 24, 2025. https://doi.org/10.1111/jedm.12428.

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AbstractItem nonresponses frequently occurs in educational and psychological assessments, and if not appropriately handled, it can undermine the reliability of the results. This study introduces a missing data model based on the missing not at random (MNAR) mechanism, incorporating the monotonic missingness assumption to capture individual‐level missingness patterns and behavioral dynamics. In specific, the cumulative number of missing indicators allows to consider the tendency of current item's missingness based on the previous missingnesses, which reduces the number of nuisance parameters fo

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