Relevant bibliographies by topics / Data missingness

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data missingness'

Author: Grafiati
Published: 24 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Data missingness"

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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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Dissertations / Theses on the topic "Data missingness"

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Cao, Yu. "Bayesian nonparametric analysis of longitudinal data with non-ignorable non-monotone missingness." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5750.

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In longitudinal studies, outcomes are measured repeatedly over time, but in reality clinical studies are full of missing data points of monotone and non-monotone nature. Often this missingness is related to the unobserved data so that it is non-ignorable. In such context, pattern-mixture model (PMM) is one popular tool to analyze the joint distribution of outcome and missingness patterns. Then the unobserved outcomes are imputed using the distribution of observed outcomes, conditioned on missing patterns. However, the existing methods suffer from model identification issues if data is sparse
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Deng, Wei. "Multiple imputation for marginal and mixed models in longitudinal data with informative missingness." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126890027.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains xiii, 108 p.; also includes graphics. Includes bibliographical references (p. 104-108). Available online via OhioLINK's ETD Center
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Andersson, Oscar, and Tim Andersson. "AI applications on healthcare data." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44752.

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The purpose of this research is to get a better understanding of how different machine learning algorithms work with different amounts of data corruption. This is important since data corruption is an overbearing issue within data collection and thus, in extension, any work that relies on the collected data. The questions we were looking at were: What feature is the most important? How significant is the correlation of features? What algorithms should be used given the data available? And, How much noise (inaccurate or unhelpful captured data) is acceptable?  The study is structured to introdu
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Hafez, Mai. "Analysis of multivariate longitudinal categorical data subject to nonrandom missingness : a latent variable approach." Thesis, London School of Economics and Political Science (University of London), 2015. http://etheses.lse.ac.uk/3184/.

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Longitudinal data are collected for studying changes across time. In social sciences, interest is often in theoretical constructs, such as attitudes, behaviour or abilities, which cannot be directly measured. In that case, multiple related manifest (observed) variables, for example survey questions or items in an ability test, are used as indicators for the constructs, which are themselves treated as latent (unobserved) variables. In this thesis, multivariate longitudinal data is considered where multiple observed variables, measured at each time point, are used as indicators for theoretical c
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Bishop, Brenden. "Examining Random-Coeffcient Pattern-Mixture Models forLongitudinal Data with Informative Dropout." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu150039066582153.

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Lee, Amra. "Why do some civilian lives matter more than others? Exploring how the quality, timeliness and consistency of data on civilian harm affects the conduct of hostilities for civilians caught in conflict." Thesis, Uppsala universitet, Institutionen för freds- och konfliktforskning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387653.

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Normatively, protecting civilians from the conduct of hostilities is grounded in the Geneva Conventions and the UN Security Council protection of civilian agenda, both of which celebrate their 70 and 20 year anniversaries in 2019. Previous research focusses heavily on protection of civilians through peacekeeping whereas this research focuses on ‘non-armed’ approaches to enhancing civilian protection in conflict. Prior research and experience reveals a high level of missingness and variation in the level of available data on civilian harm in conflict. Where civilian harm is considered in the pe
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Poleto, Frederico Zanqueta. "Análise de dados categorizados com omissão em variáveis explicativas e respostas." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-09052011-000104/.

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Nesta tese apresentam-se desenvolvimentos metodológicos para analisar dados com omissão e também estudos delineados para compreender os resultados de tais análises. Escrutinam-se análises de sensibilidade bayesiana e clássica para dados com respostas categorizadas sujeitas a omissão. Mostra-se que as componentes subjetivas de cada abordagem podem influenciar os resultados de maneira não-trivial, independentemente do tamanho da amostra, e que, portanto, as conclusões devem ser cuidadosamente avaliadas. Especificamente, demonstra-se que distribuições \\apriori\\ comumente consideradas como não-
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Park, Soomin. "Analysis of longitudinal data with informative missingness." 2001. http://www.library.wisc.edu/databases/connect/dissertations.html.

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Chang, Yu-Ping, and 張育萍. "Geonme-wide pattern of informative missingness using HapMap data." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/11567638822940451863.

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碩士<br>國立陽明大學<br>公共衛生研究所<br>101<br>Objectives: This dissertation aims to explore the genome-wide pattern of informative missingness among parent genotypes due to various qualities of genotyping. Methods: Genotype, quality score, and pedigree of HapMap data were merged together and genotype scores below 10000, 9000, 8000, and 7000 were assigned to be missing values. Therefore, four sets of trio data with partial missing parental genotypes were implemented by the TIMBD (Guo, 2012), which determines whether parental genotypes are missing informatively or not. SNPs that are significant in the four
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Costa, Adriana Isabel Fonseca. "A study on missing data: handing missingness using Denoising Autoencoders." Master's thesis, 2018. http://hdl.handle.net/10316/86262.

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Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia<br>Com a evolução tecnológica, verificou-se um aumento exponencial da quantidade de dados recolhidos e armazenados. Assim, surgiu a necessidade de criar mecanismos automáticos para extrair conhecimento dos referidos dados. Estes mecanismos automáticos, conhecidos por modelos de aprendizagem automática, foram, na sua maioria, desenvolvidos para dados completos, requisito que nem sempre é possível cumprir. Neste contexto, a imputação dos dados (substituição dos valores em falta por
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Books on the topic "Data missingness"

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Benstead, Lindsay J. Survey Research in the Arab World. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.14.

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Since the first surveys were conducted there in the late 1980s, survey research has expanded rapidly in the Arab world. Almost every country in the region is now included in the Arab Barometer, Afrobarometer, or World Values Survey. Moreover, the Arab spring marked a watershed, with the inclusion of Tunisia and Libya and addition of many topics, such as voting behavior, that were previously considered too sensitive. As a result, political scientists have dozens of largely untapped data sets to answer theoretical and policy questions. To make progress toward measuring and reducing total survey
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Book chapters on the topic "Data missingness"

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Starbuck, Craig. "Data Preparation." In The Fundamentals of People Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28674-2_6.

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AbstractThis chapter explains data architecture concepts needed to properly integrate and extract data from analytics platforms as well as methods of screening and cleaning data (e.g., missingness, outliers, data binning, one-hot encoding, feature engineering).
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Laaksonen, Seppo. "Missingness, Its Reasons and Treatment." In Survey Methodology and Missing Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-79011-4_7.

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Daniels, Michael J., Antonio Linero, and Jason Roy. "DPMs for non-monotone missingness." In Bayesian Nonparametrics for Causal Inference and Missing Data. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9780429324222-12.

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Laaksonen, Seppo. "Sampling Principles, Missingness Mechanisms, and Design Weighting." In Survey Methodology and Missing Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-79011-4_4.

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Rodrigues de Morais, Sérgio, and Alex Aussem. "Exploiting Data Missingness in Bayesian Network Modeling." In Advances in Intelligent Data Analysis VIII. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03915-7_4.

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Losardo, Diane, Sy-Miin Chow, A. T. Panter, Melissa Burkley, and Edward Burkley. "Ecological Momentary Assessment (EMA) Designs with Planned Missingness." In Dependent Data in Social Sciences Research. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-56318-8_26.

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Ting, Clement Pek Wen, and Patrick Hang Hui Then. "Feature Reduction of Relational Oil Drilling Data Before Propositionalization and Harmonization by Measuring Relational Data Missingness." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5547-3_4.

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"Case Studies: Ignorable Missingness." In Missing Data in Longitudinal Studies. Chapman and Hall/CRC, 2008. http://dx.doi.org/10.1201/9781420011180-11.

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"Case Studies: Nonignorable Missingness." In Missing Data in Longitudinal Studies. Chapman and Hall/CRC, 2008. http://dx.doi.org/10.1201/9781420011180-14.

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"Models for Handling Nonignorable Missingness." In Missing Data in Longitudinal Studies. Chapman and Hall/CRC, 2008. http://dx.doi.org/10.1201/9781420011180-12.

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Conference papers on the topic "Data missingness"

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Hao, Yiyan, Megan T. Jones, Simon Vandekar, Russell T. Shinohara, and Brian R. White. "Addressing Missing Data With Multiple Imputation in Optical Neuroimaging." In Bio-Optics: Design and Application. Optica Publishing Group, 2025. https://doi.org/10.1364/boda.2025.jm4a.8.

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In optical neuroimaging, the field-of-view invariably differs across subjects. Here, we evaluate the use of techniques to address missingness, including multiple imputation, with an emphasis on their effects on Type I and Type II error.
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Villanueva, Julissa, and Denis Mauá. "Tractable Classification with Non-Ignorable Missing Data Using Generative Random Forests." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227969.

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Missing data is abundant in predictive tasks. Typical approaches assume that the missingness process is ignorable or non-informative and handle missing data either by marginalization or heuristically. Yet, data is often missing in a non-ignorable way, which introduce bias in prediction. In this paper, we develop a new method to perform tractable predictive inference under non-ignorable missing data using probabilistic circuits derived from Decision Tree Classifiers and a partially specified response model of missingness. We show empirically that our method delivers less biased (probabilistic)
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Ghorbani, Amirata, and James Y. Zou. "Embedding for Informative Missingness: Deep Learning With Incomplete Data." In 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2018. http://dx.doi.org/10.1109/allerton.2018.8636008.

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Oltjen, William C., Yangxin Fan, Jiqi Liu, et al. "FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data." In 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC). IEEE, 2022. http://dx.doi.org/10.1109/pvsc48317.2022.9938523.

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Oltjen, William, Yangxin Fan, Jiqi Liu, et al. "FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data." In Photovoltaics Specialists Conference. US DOE, 2022. http://dx.doi.org/10.2172/1959064.

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Buttner, Maik, Christian Beyer, and Myra Spiliopoulou. "Reducing Missingness in a Stream through Cost-Aware Active Feature Acquisition." In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2022. http://dx.doi.org/10.1109/dsaa54385.2022.10032414.

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Koyuncu, Deniz, Alex Gittens, Bülent Yener, and Moti Yung. "Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2023. http://dx.doi.org/10.1145/3580305.3599297.

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Oltjen, William, Yangxin Fan, Jiqi Liu, et al. "FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data." In PV Reliability Workshop. US DOE, 2022. http://dx.doi.org/10.2172/1959042.

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Mohan, Karthika, Felix Thoemmes, and Judea Pearl. "Estimation with Incomplete Data: The Linear Case." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/705.

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Traditional methods for handling incomplete data, including Multiple Imputation and Maximum Likelihood, require that the data be Missing At Random (MAR). In most cases, however, missingness in a variable depends on the underlying value of that variable. In this work, we devise model-based methods to consistently estimate mean, variance and covariance given data that are Missing Not At Random (MNAR). While previous work on MNAR data require variables to be discrete, we extend the analysis to continuous variables drawn from Gaussian distributions. We demonstrate the merits of our techniques by c
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Callahan, Sequoia. "Ecologically Plausible? Comparing the Independent and Paired Samples t-Test With Nonrandom Missingness and Skewed Data." In 2021 AERA Annual Meeting. AERA, 2021. http://dx.doi.org/10.3102/1691413.

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Reports on the topic "Data missingness"

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Huang, Lei, Meng Song, Hui Shen, et al. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48221.

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One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advan
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