Relevant bibliographies by topics / Data missingness

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

Academic literature on the topic 'Data missingness'

Author: Grafiati
Published: 24 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Data missingness"

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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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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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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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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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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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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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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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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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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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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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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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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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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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"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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Wuy, Margaret, and Paul Albert. "Analysis of Longitudinal Data with Missingness*." In Advances in Clinical Trial Biostatistics. CRC Press, 2003. http://dx.doi.org/10.1201/9780203912881.ch11.

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Daniels, Michael J., and Dandan Xu. "Bayesian Methods for Longitudinal Data with Missingness." In Bayesian Methods in Pharmaceutical Research. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315180212-9.

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"Imputing Prociency Data under Planned Missingness in Population Models." In Handbook of International Large-Scale Assessment. Chapman and Hall/CRC, 2013. http://dx.doi.org/10.1201/b16061-13.

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Collins, Tim, Sandra I. Woolley, Salome Oniani, and Anand Pandyan. "Quantifying Missingness in Wearable Heart Rate Recordings." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210352.

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Wrist-worn photoplethysmography (PPG) heart rate monitoring devices are increasingly used in clinical applications despite the potential for data missingness and inaccuracy. This paper provides an analysis of the intermittency of experimental wearable data recordings. Devices recorded heart rate with gaps of 5 or more minutes 41.6% of the time and 15 or more minutes 3.8% of the time.
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Conference papers on the topic "Data missingness"

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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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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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