Relevant bibliographies by topics / Data imputation

Contents

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

Academic literature on the topic 'Data imputation'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Data imputation"

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Lu, Kaifeng. "Number of imputations needed to stabilize estimated treatment difference in longitudinal data analysis." Statistical Methods in Medical Research 26, no. 2 (2014): 674–90. http://dx.doi.org/10.1177/0962280214554439.

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Multiple imputation procedures replace each missing value with a set of plausible values based on the posterior predictive distribution of missing data given observed data. In many applications, as few as five imputations are adequate to achieve high efficiency relative to an infinite number of imputations. However, substantially more imputations are often needed to stabilize imputation-based inference at the analysis stage. Imputation-based inference at the analysis stage is considered stable if the conditional variability of the multiple imputation estimator, half-width of 95% confidence int
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Zhong, Ming, Satish Sharma, and Zhaobin Liu. "Assessing Robustness of Imputation Models Based on Data from Different Jurisdictions." Transportation Research Record: Journal of the Transportation Research Board 1917, no. 1 (2005): 116–26. http://dx.doi.org/10.1177/0361198105191700114.

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The literature indicates that many highway and transportation agencies in North America and Europe estimate missing values in their collected traffic data records. Estimating missing values is known as data imputation. Such a convention can be traced back to early traffic monitoring systems in the 1930s; however, no studies have been found to assess the accuracy of imputations carried out by transportation practitioners. The imputation methods used by highway agencies are varied and intuitive in nature. Some of them could result in large imputation errors in certain circumstances. Those errors
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Seu, Kimseth, Mi-Sun Kang, and HwaMin Lee. "An Intelligent Missing Data Imputation Techniques: A Review." JOIV : International Journal on Informatics Visualization 6, no. 1-2 (2022): 278. http://dx.doi.org/10.30630/joiv.6.1-2.935.

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The incomplete dataset is an unescapable problem in data preprocessing that primarily machine learning algorithms could not employ to train the model. Various data imputation approaches were proposed and challenged each other to resolve this problem. These imputations were established to predict the most appropriate value using different machine learning algorithms with various concepts. Furthermore, accurate estimation of the imputation method is exceptionally critical for some datasets to complete the missing value, especially imputing datasets in medical data. The purpose of this paper is t
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Schnetzer, Matthias, Franz Astleithner, Predrag Cetkovic, Stefan Humer, Manuela Lenk, and Mathias Moser. "Quality Assessment of Imputations in Administrative Data." Journal of Official Statistics 31, no. 2 (2015): 231–47. http://dx.doi.org/10.1515/jos-2015-0015.

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Abstract This article contributes a framework for the quality assessment of imputations within a broader structure to evaluate the quality of register-based data. Four quality-related hyperdimensions examine the data processing from the raw-data level to the final statistics. Our focus lies on the quality assessment of different imputation steps and their influence on overall data quality. We suggest classification rates as a measure of accuracy of imputation and derive several computational approaches.
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Pandey, Ritesh Kumar, and Dr Asha Ambhaikar. "Data Imputation by Soft Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 808–10. http://dx.doi.org/10.31142/ijtsrd14112.

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Pandey, Ritesh Kumar, and Dr Asha Ambhaikar. "Data Imputation Methods and Technologies." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 828–31. http://dx.doi.org/10.31142/ijtsrd14113.

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Meng, Haoyu. "A Comparative Study on Missing Value Imputation Techniques in Machine Learning." SHS Web of Conferences 218 (2025): 02014. https://doi.org/10.1051/shsconf/202521802014.

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Handling missing values is a crucial step in data preprocessing, as incomplete data can significantly impact model performance and overall data integrity. This study explores and compares various missing value imputation techniques, including deletion methods, simple imputations (mean, median), machine learning-based approaches (k-Nearest Neighbors (k-NN), multiple imputation), and hybrid strategies. The research utilizes an extensive dataset from National Football League Play-by-Play, implementing these techniques and evaluating their effectiveness using Root Mean Squared Error (RMSE) as the
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Xue, Jianing. "Review on data imputation methods in machine learning." Journal of Physics: Conference Series 2646, no. 1 (2023): 012034. http://dx.doi.org/10.1088/1742-6596/2646/1/012034.

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Abstract Data is an important element in the analysis of machine learning. It is usually measured based on observations and is also an indispensable element in training a model. Good preparation of data helps enhance the performance of analysis and is able to deliver reliable final results. However, lots of factors influence the dataset and some lead to the loss of some data. When some portion of the data is missing, it causes biases in the final prediction outcomes. In order to minimize the consequences of missing data, several data imputation methods are established to solve the problem. Thi
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A. Abassi, Rahibu, Amina S. Msengwa, and Rocky R. J. Akarro. "Imputation methods on retrospective breast cancer data in Tanzania: A comparative study." Women Health Care and Issues 5, no. 4 (2022): 01–09. http://dx.doi.org/10.31579/2642-9756/118.

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Background: Clinical datasets are at risk of having missing data for several reasons including patients’ failure to attend clinical measurements and measurement recorder’s defects. Missing data can significantly affect the analysis and results might be doubtful due to bias caused by omission incomplete records during analysis especially if a dataset is small. This study aims to compare several imputation methods in terms of efficiency in filling-in missing data so as to increase prediction and classification accuracy in breast cancer dataset. Methodology: Five imputation methods namely series
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Zhao, Yuxuan, Eric Landgrebe, Eliot Shekhtman, and Madeleine Udell. "Online Missing Value Imputation and Change Point Detection with the Gaussian Copula." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 9199–207. http://dx.doi.org/10.1609/aaai.v36i8.20906.

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Missing value imputation is crucial for real-world data science workflows. Imputation is harder in the online setting, as it requires the imputation method itself to be able to evolve over time. For practical applications, imputation algorithms should produce imputations that match the true data distribution, handle data of mixed types, including ordinal, boolean, and continuous variables, and scale to large datasets. In this work we develop a new online imputation algorithm for mixed data using the Gaussian copula. The online Gaussian copula model produces meets all the desiderata: its imputa
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Dissertations / Theses on the topic "Data imputation"

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DiCesare, Giuseppe. "Imputation, Estimation and Missing Data in Finance." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2920.

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Suppose <em>X</em> is a diffusion process, possibly multivariate, and suppose that there are various segments of the components of <em>X</em> that are missing. This happens, for example, if <em>X</em> is the price of various assets and these prices are only observed at specific discrete trading times. Imputation (or conditional simulation) of the missing pieces of the sample paths of <em>X</em> is discussed in several settings. When <em>X</em> is a Brownian motion the conditioned process is a tied down Brownian motion or a Brownian bridge process. In the special case of Gaussian st
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Wall, Tobias, and Jacob Titus. "Imputation and Generation of Multidimensional Market Data." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184162.

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Market risk is one of the most prevailing risks to which financial institutions are exposed. The most popular approach in quantifying market risk is through Value at Risk. Organisations and regulators often require a long historical horizon of the affecting financial variables to estimate the risk exposures. A long horizon stresses the completeness of the available data; something risk applications need to handle.  The goal of this thesis is to evaluate and propose methods to impute financial time series. The performance of the methods will be measured with respect to both price-, and risk met
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Karangwa, Innocent. "Imputation techniques for non-ordered categorical missing data." University of the Western Cape, 2016. http://hdl.handle.net/11394/5061.

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Philosophiae Doctor - PhD<br>Missing data are common in survey data sets. Enrolled subjects do not often have data recorded for all variables of interest. The inappropriate handling of missing data may lead to bias in the estimates and incorrect inferences. Therefore, special attention is needed when analysing incomplete data. The multivariate normal imputation (MVNI) and the multiple imputation by chained equations (MICE) have emerged as the best techniques to impute or fills in missing data. The former assumes a normal distribution of the variables in the imputation model, but can also hand
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Zhang, Paul. "Multiple imputation of missing data in clinical trials." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ63596.pdf.

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Tempelman, Dina Catharina Geertruida. "Imputation of restricted data applications to business surveys /." [S.l. : [Groningen : s.n.] ; University Library Groningen] [Host], 2007. http://irs.ub.rug.nl/ppn/29900418X.

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Brydon, Humphrey Charles. "Missing imputation methods explored in big data analytics." University of the Western Cape, 2018. http://hdl.handle.net/11394/6605.

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Philosophiae Doctor - PhD (Statistics and Population Studies)<br>The aim of this study is to look at the methods and processes involved in imputing missing data and more specifically, complete missing blocks of data. A further aim of this study is to look at the effect that the imputed data has on the accuracy of various predictive models constructed on the imputed data and hence determine if the imputation method involved is suitable. The identification of the missingness mechanism present in the data should be the first process to follow in order to identify a possible imputation method. The
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Quartagno, M. "Multiple imputation for individual patient data meta-analyses." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2016. http://researchonline.lshtm.ac.uk/3141186/.

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The term meta-analysis refers to a set of statistical techniques for combining findings from different studies in order to draw more definitive conclusions about some treatment or exposure effect of interest in a particular context. Recently, meta-analyses which aim to combine the individual observations collected in each study, instead of simple summary measures, have been gaining in popularity in medical research. The main advantage of this so-called Individual Patient Data Meta-Analyses (IPD-MA) is that they have much more statistical power to investigate heterogeneity of the contributing s
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Smuk, M. "Missing data methodology : sensitivity analysis after multiple imputation." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2015. http://researchonline.lshtm.ac.uk/2212896/.

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Within epidemiological and clinical research, missing data are a common issue which are often inappropriately handled in practice. Multiple imputation (MI) is a popular tool used to 'fill in' partially observed data with plausible values drawn from an appropriate imputation distribution. Software generally implements MI under the assumption that data are 'missing at random' (MAR) i.e. that the missing mechanism is not dependent on the missing data conditional on the observed data. This is a strong inherently untestable assumption, and if incorrect can result in misleading inferences. The sensi
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D'Alberto, Riccardo <1988&gt. "Statistical Matching Imputation among Different Farm Data Sources." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amsdottorato.unibo.it/7788/1/R_DAlberto_PhD.pdf.

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This work addresses the challenge of integrating different data sources, dealing with both statistical methodology and a practical application to farm data. It reviews the existing literature on Statistical Matching (SM) imputation, focusing on non-parametric micro “hot deck” techniques, which reduce the bias generated by model-based integration approaches. Implementing new combinations of these techniques with not commonly applied distance functions, we propose a strategy for the imputation goodness validation (missing in the SM imputation literature) corroborating the few common prescription
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Miranda, Samantha. "Investigation of Multiple Imputation Methods for Categorical Variables." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/etd/3722.

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We compare different multiple imputation methods for categorical variables using the MICE package in R. We take a complete data set and remove different levels of missingness and evaluate the imputation methods for each level of missingness. Logistic regression imputation and linear discriminant analysis (LDA) are used for binary variables. Multinomial logit imputation and LDA are used for nominal variables while ordered logit imputation and LDA are used for ordinal variables. After imputation, the regression coefficients, percent deviation index (PDI) values, and relative frequency tables wer
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Books on the topic "Data imputation"

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Sarah, Dipko, Urban Institute, Westat inc, Assessing the New Federalism (Program), and Child Trends Incorporated, eds. 1997 NSAF data editing and imputation. Urban Institute, 1999.

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van Buuren, Stef. Flexible Imputation of Missing Data, Second Edition. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429492259.

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de Waal, Ton, Jeroen Pannekoek, and Sander Scholtus. Handbook of Statistical Data Editing and Imputation. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470904848.

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Teegavarapu, Ramesh S. V. Imputation Methods for Missing Hydrometeorological Data Estimation. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60946-6.

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1951-, Pannekoek Jeroen, and Scholtus Sander 1983-, eds. Handbook of statistical data editing and imputation. Wiley, 2011.

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Schreuder, Hans T. Data estimation and prediction for natural resources public data. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 1998.

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M, Reich Robin, and Rocky Mountain Research Station (Fort Collins, Colo.), eds. Data estimation and prediction for natural resources public data. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 1998.

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Carpenter, James R. Multiple imputation and its application. John Wiley & Sons, 2013.

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Boxhill, Walton O. 1981 shelter cost data: Editing and imputation strategies. Minister of Supply and Services Canada, 1985.

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(Statistician), Shao Jun, ed. Statistical methods for handling incomplete data. CRC Press, Taylor & Francis Group, 2014.

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Book chapters on the topic "Data imputation"

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Tkachenko, Nikita. "Imputation." In Data Insight Foundations. Apress, 2024. https://doi.org/10.1007/979-8-8688-0580-6_5.

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Kim, Jae Kwang, and Jun Shao. "Imputation." In Statistical Methods for Handling Incomplete Data, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429321740-4.

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Ratitch, Bohdana. "Multiple Imputation." In Clinical Trials with Missing Data. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118762516.ch6.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Missing Data Imputation." In Clinical Data Analysis on a Pocket Calculator. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27104-0_17.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Missing Data Imputation." In Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4704-3_3.

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Kim, Jae Kwang, and Jun Shao. "Multiple Imputation." In Statistical Methods for Handling Incomplete Data, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429321740-5.

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Kim, Jae Kwang, and Jun Shao. "Fractional Imputation." In Statistical Methods for Handling Incomplete Data, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429321740-6.

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Raghunathan, Trivellore, Patricia A. Berglund, and Peter W. Solenberger. "Categorical Data Analysis." In Multiple Imputation in Practice. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315154275-5.

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Raghunathan, Trivellore, Patricia A. Berglund, and Peter W. Solenberger. "Longitudinal Data Analysis." In Multiple Imputation in Practice. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315154275-8.

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Little, Roderick J. A., and Donald B. Rubin. "Single Imputation Methods." In Statistical Analysis with Missing Data. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119013563.ch4.

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Conference papers on the topic "Data imputation"

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Bansal, Ankit, Praneeth Reddy Amudala Puchakayala, Swathi Suddala, Ruchi Bansal, and Aradhya Singhal. "Missing Value Imputation using Spatio-Convolutional Generative Adversarial Imputation Network." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070761.

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Li, Shuang, Xianglong Luo, Jiayu Yang, Zhongcheng Xu, and Ruochen Liu. "Traffic Flow Data Imputation Based on Feature Fusion Attention Imputation Network." In 2023 IEEE 8th International Conference on Intelligent Transportation Engineering (ICITE). IEEE, 2023. http://dx.doi.org/10.1109/icite59717.2023.10733922.

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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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Yarramsetti, Harshitaa, Kritika Javali, Mukund Logasundar, Richard Li, and Ranga Raju Vatsavai. "A Deep Learning Based Cloud Imputation Framework." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00108.

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Sharaf, Mohamed A., Nishi Kochunni, Heba Helal, and Omar El Harrouss. "CoDAQ: Congressional-Based Data Imputation for Aggregate Queries." In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2025. https://doi.org/10.1109/bigcomp64353.2025.00060.

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kuang, shenfen. "Unsupervised data imputation via matrix-variate variational autoencoders." In Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), edited by Lvqing Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3037941.

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Lu, Jianbin, Shuai Han, Weidong Chen, and Leping Sun. "Denoising Diffusion Probabilistic Imputation Model for Tabular Data." In 2024 IEEE First International Conference on Data Intelligence and Innovative Application (DIIA). IEEE, 2024. https://doi.org/10.1109/diia62678.2024.10871307.

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Singh, Aryan Kumar, Arpit Saikia, Pranita Baro, and Malaya Dutta Borah. "Transformer-Based Self-Supervised Imputation for Medical Data." In 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 2024. https://doi.org/10.1109/iceccme62383.2024.10796511.

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Yin, Yue, Jiaoyun Yang, and Ning An. "CauImputation: Utilizing Structural Causal Model in Data Imputation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822034.

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Khamis, Abdelwahed, and Sara Khalifa. "NeuralPrefix: A Zero-shot Sensory Data Imputation Plugin." In 2025 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 2025. https://doi.org/10.1109/percom64205.2025.00037.

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Reports on the topic "Data imputation"

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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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Wei, Ting, and Jon Fricker. Weigh-In-Motion Data Checking and Imputation. Purdue University, 2003. http://dx.doi.org/10.5703/1288284313349.

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Zaninotto, Paola. Multiple Imputation by Chained Equations (MICE). Instats Inc., 2024. https://doi.org/10.61700/1tr36kp5gwa5b1858.

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This seminar provides a comprehensive introduction to Multiple Imputation by Chained Equations (MICE) in R, offering researchers advanced skills to effectively handle missing data using robust statistical techniques. Led by Dr. Paola Zaninotto from University College London, participants will gain proficiency in applying MICE across various research disciplines, enhancing data analysis and interpretation capabilities.
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Sukasih, Amang S., and Victoria Scott. Cyclical Tree-Based Hot Deck Imputation. RTI Press, 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0052.2307.

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Hot deck imputation is a method for filling in a missing value in a survey item (item nonrespondent) with a valid reported value from a donor (item respondent) within the survey. Our paper presents a multivariate hot deck imputation method called Cyclical Tree-Based Hot Deck (CTBHD). This method was developed to handle missing values in complex survey data with many different types of variables and allows the user to customize imputation classes, use sorting variables, impute vectors and compositional variables, and even edit or recode data “on-the-fly.” Additionally, CTBHD employs a cycling a
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White, T. Kirk, Jerome Reiter, and Amil Petrin. Plant-level Productivity and Imputation of Missing Data in U.S. Census Manufacturing Data. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w17816.

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Ivanic, Maros. Reconciliation of the GTAP and Household Survey Data. GTAP Research Memoranda, 2004. http://dx.doi.org/10.21642/gtap.rm05.

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This paper presents a method that was employed in order to make the available fourteen household income survey data sets compatible with the data in the GTAP version 5 data base. The first step of the method was the imputation of the unobservable returns to GTAP factors from the reported data. The second step was the reconciliation of the two data sets so that their joint totals would be identical. The paper does not claim to be the final word on either data imputation or reconciliation; instead it works through the various issues encountered in the process, proposes solutions to them and leav
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White, T. Kirk, Jerome Reiter, and Amil Petrin. Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w22569.

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Oliveira, Rodrigo C., Jesse Lastunen, Enrico Nichelatti, and Pia Rattenhuber. Imputation methods for adjusting SOUTHMOD input data to income losses due to the COVID-19 crisis. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/wtn/2021-19.

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Yelchuri, Srinath, A. Rangaraj, Yu Xie, et al. A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1837967.

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Win, Nu Nu, Jonathan Hambur, and Robert Breunig. Are Investment Tax Breaks Effective? Australian Evidence. Reserve Bank of Australia, 2025. https://doi.org/10.47688/rdp2025-01.

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Using Australian tax and survey data, we exploit discrete eligibility cut-offs to estimate the effect of several business investment tax breaks, including tax credits and instant asset write-offs, implemented over the past 15 years. Policies implemented during the global financial crisis increased investment. Responses are larger for unincorporated businesses, possibly reflecting reduced efficacy of investment stimulus under Australia's dividend imputation system. However, we find mostly no evidence of an effect for other investment policies, including those implemented to address the COVID-19
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