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Relevant bibliographies by topics / Imputation methods

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Academic literature on the topic 'Imputation methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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Journal articles on the topic "Imputation methods"

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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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Poyatos, Rafael, Oliver Sus, Llorenç Badiella, Maurizio Mencuccini, and Jordi Martínez-Vilalta. "Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information." Biogeosciences 15, no. 9 (2018): 2601–17. http://dx.doi.org/10.5194/bg-15-2601-2018.

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Abstract. The ubiquity of missing data in plant trait databases may hinder trait-based analyses of ecological patterns and processes. Spatially explicit datasets with information on intraspecific trait variability are rare but offer great promise in improving our understanding of functional biogeography. At the same time, they offer specific challenges in terms of data imputation. Here we compare statistical imputation approaches, using varying levels of environmental information, for five plant traits (leaf biomass to sapwood area ratio, leaf nitrogen content, maximum tree height, leaf mass p

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Lee, Do-Hyun, Saem-Ee Woo, Min-Woong Jung, and Tae-Young Heo. "Evaluation of Odor Prediction Model Performance and Variable Importance according to Various Missing Imputation Methods." Applied Sciences 12, no. 6 (2022): 2826. http://dx.doi.org/10.3390/app12062826.

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The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number of data and a large number of missing data. First, we compared the data removal and imputation methods, and the method of imputing missing data was found to be more effective. Then, in order to recommend a suitable model, we created a total of 126 models (missing imputation: single imputation, multiple imputations, K-nearest neighbor imputation; data preprocessing: standardization, principal component analysis, partial least square; and predictive method:

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Misztal, Małgorzata Aleksandra. "Comparison of Selected Multiple Imputation Methods for Continuous Variables – Preliminary Simulation Study Results." Acta Universitatis Lodziensis. Folia Oeconomica 6, no. 339 (2019): 73–98. http://dx.doi.org/10.18778/0208-6018.339.05.

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The problem of incomplete data and its implications for drawing valid conclusions from statistical analyses is not related to any particular scientific domain, it arises in economics, sociology, education, behavioural sciences or medicine. Almost all standard statistical methods presume that every object has information on every variable to be included in the analysis and the typical approach to missing data is simply to delete them. However, this leads to ineffective and biased analysis results and is not recommended in the literature. The state of the art technique for handling missing data

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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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Corder, Nathan, and Shu Yang. "Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness." Journal of Causal Inference 8, no. 1 (2020): 249–71. http://dx.doi.org/10.1515/jci-2019-0024.

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Abstract The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Alternatively, fractional imputation, proposed by Kim 2011, has been implemented to handling missing values in regression context. In this article, we develop fractional imputation methods for estimating the average treatment effects with confounders missing at random. We show that the fracti

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Biernacka, Joanna M., Rui Tang, Jia Li, et al. "Assessment of genotype imputation methods." BMC Proceedings 3, Suppl 7 (2009): S5. http://dx.doi.org/10.1186/1753-6561-3-s7-s5.

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Schober, Patrick, and Thomas R. Vetter. "Missing Data and Imputation Methods." Anesthesia & Analgesia 131, no. 5 (2020): 1419–20. http://dx.doi.org/10.1213/ane.0000000000005068.

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Eekhout, Iris, Henrica CW de Vet, Michiel R. de Boer, Jos WR Twisk, and Martijn W. Heymans. "Passive imputation and parcel summaries are both valid to handle missing items in studies with many multi-item scales." Statistical Methods in Medical Research 27, no. 4 (2016): 1128–40. http://dx.doi.org/10.1177/0962280216654511.

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Previous studies showed that missing data in multi-item scales can best be handled by multiple imputation of item scores. However, when many scales are used, the number of items will become too large for the imputation model to reliably estimate imputations. A solution is to use passive imputation or a parcel summary score that combine and consequently reduce the number of variables in the imputation model. The performance of these methods was evaluated in a simulation study and illustrated in an example. Passive imputation, which updated scale scores from imputed items, and parcel summary sco

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Klímová, Anita, Eva Kašná, Karolína Machová, Michaela Brzáková, Josef Přibyl, and Luboš Vostrý. "The use of genomic data and imputation methods in dairy cattle breeding." Czech Journal of Animal Science 65, No. 12 (2020): 445–53. http://dx.doi.org/10.17221/83/2020-cjas.

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The inclusion of animal genotype data has contributed to the development of genomic selection. Animals are selected not only based on pedigree and phenotypic data but also on the basis of information about their genotypes. Genomic information helps to increase the accuracy of selection of young animals and thus enables a reduction of the generation interval. Obtaining information about genotypes in the form of SNPs (single nucleotide polymorphisms) has led to the development of new chips for genotyping. Several methods of genomic comparison have been developed as a result. One of the methods i

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Dissertations / Theses on the topic "Imputation methods"

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Mittinty, Murthy N. "Nearest neighbour imputation and variance estimation methods." Thesis, University of Canterbury. Mathematics and Statistics, 2004. http://hdl.handle.net/10092/5643.

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In large-scale surveys, non-response is a common phenomenon. This non-response can be of two types; unit and item non-response. In this thesis we deal with item non-response as other responses from the survey unit can be used for adjustment. Usually non-response adjustment is carried out in one of three ways; weighting, imputation and no adjustments. Imputation is the most commonly used adjustment method, either as single imputation or multiple imputations. In this thesis we study single imputation, in particular nearest neighbour methods, and we have developed a new method. Our method is base

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Zhao, Xinqiang. "Imputation by neural networks and related methods." Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252325.

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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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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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Heidt, Kaitlyn. "Comparison of Imputation Methods for Mixed Data Missing at Random." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3559.

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A statistician's job is to produce statistical models. When these models are precise and unbiased, we can relate them to new data appropriately. However, when data sets have missing values, assumptions to statistical methods are violated and produce biased results. The statistician's objective is to implement methods that produce unbiased and accurate results. Research in missing data is becoming popular as modern methods that produce unbiased and accurate results are emerging, such as MICE in R, a statistical software. Using real data, we compare four common imputation methods, in the MICE pa

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Roshyara, Nab Raj, Katrin Horn, Holger Kirsten, Peter Ahnert, and Markus Scholz. "Comparing performance of modern genotype imputation methods in different ethnicities." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-213865.

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A variety of modern software packages are available for genotype imputation relying on advanced concepts such as pre-phasing of the target dataset or utilization of admixed reference panels. In this study, we performed a comprehensive evaluation of the accuracy of modern imputation methods on the basis of the publicly available POPRES samples. Good quality genotypes were masked and re-imputed by different imputation frameworks: namely MaCH, IMPUTE2, MaCH-Minimac, SHAPEIT-IMPUTE2 and MaCH-Admix. Results were compared to evaluate the relative merit of pre-phasing and the usage of admixed referen

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Chan, Pui-shan, and 陳佩珊. "On the use of multiple imputation in handling missing values in longitudinal studies." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B45009879.

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Matsouaka, Roland Albert. "Contributions to Imputation Methods Based on Ranks and to Treatment Selection Methods in Personalized Medicine." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10078.

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The chapters of this thesis focus two different issues that arise in clinical trials and propose novel methods to address them. The first issue arises in the analysis of data with non-ignorable missing observations. The second issue concerns the development of methods that provide physicians better tools to understand and treat diseases efficiently by using each patient's characteristics and personal biomedical profile. Inherent to most clinical trials is the issue of missing data, specially those that arise when patients drop out the study without further measurements. Proper handling of miss

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Aslan, Sipan. "Comparison Of Missing Value Imputation Methods For Meteorological Time Series Data." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612426/index.pdf.

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Dealing with missing data in spatio-temporal time series constitutes important branch of general missing data problem. Since the statistical properties of time-dependent data characterized by sequentiality of observations then any interruption of consecutiveness in time series will cause severe problems. In order to make reliable analyses in this case missing data must be handled cautiously without disturbing the series statistical properties, mainly as temporal and spatial dependencies. In this study we aimed to compare several imputation methods for the appropriate completion of missing val

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Pan, Wensi. "Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175772.

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In this article, we propose an overview of missing data problem, introduce three missing data mechanisms and study general solutions to them when estimating a linear regression equation. When we have partly missing data, there are two common ways to solve this problem. One way is to ignore those records with missing values. Another method is to impute those observations being missed. Imputation methods arepreferred since they provide full datasets. We observed that there is not a general imputation solution in missing not at random (MNAR) mechanism. In order to check the performance of existin

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Books on the topic "Imputation methods"

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

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Stata multiple-imputation reference manual: Release 12. Stata Press, 2011.

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LP, StataCorp. Stata multiple-imputation reference manual: Release 11. Stata Press, 2009.

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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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Huisman, Mark. Item nonresponse: Occurrence, causes, and imputation of missing answers to test items. DSWO Press, 1999.

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Drechsler, Jörg. Synthetic datasets for statistical disclosure control: Theory and implementation. Springer, 2011.

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Subramanian, Rajesh. Transitioning to multiple imputation: A new method to impute missing blood alcohol concentration (BAC) values in FARS. National Highway Traffic Safety Administration, National Center for Statistics and Analysis, 2002.

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LP, StataCorp, ed. Stata multiple-imputation reference manual: Release 11. Stata Press, 2009.

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Dang, Hai-Anh H., and Paolo Verme. Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity? World Bank, Washington, DC, 2019. http://dx.doi.org/10.1596/1813-9450-9076.

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Book chapters on the topic "Imputation methods"

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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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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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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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Kovar, John G., and Patricia J. Whitridge. "Imputation of Business Survey Data." In Business Survey Methods. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118150504.ch22.

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Laaksonen, Seppo. "Imputation Methods for Single Variables." In Survey Methodology and Missing Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-79011-4_12.

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Mattei, Alessandra, Fabrizia Mealli, and Donald B. Rubin. "Missing Data and Imputation Methods." In Modern Analysis of Customer Surveys. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119961154.ch8.

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Momeni, Amir, Matthew Pincus, and Jenny Libien. "Imputation and Missing Data." In Introduction to Statistical Methods in Pathology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60543-2_8.

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Schafer, Joseph L. "Multiple imputation with PAN." In New methods for the analysis of change. American Psychological Association, 2001. http://dx.doi.org/10.1037/10409-012.

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Phocas, Florence. "Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools." In Methods in Molecular Biology. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2205-6_4.

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Conference papers on the topic "Imputation methods"

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S. Resheff, Yehezkel, and Daphna Weinshal. "Optimized Linear Imputation." In 6th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006092900170025.

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Umathe, Vaishali H., and Gauri Chaudhary. "Imputation methods for incomplete data." In 2015 International Conference on Innovations in Information,Embedded and Communication Systems (ICIIECS). IEEE, 2015. http://dx.doi.org/10.1109/iciiecs.2015.7193063.

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Buschjäger, Sebastian, Thomas Liebig, and Katharina Morik. "Gaussian Model Trees for Traffic Imputation." In 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007690502430254.

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Luo, Yonghong, Ying Zhang, Xiangrui Cai, and Xiaojie Yuan. "E²GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/429.

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The missing values, appear in most of multivariate time series, prevent advanced analysis of multivariate time series data. Existing imputation approaches try to deal with missing values by deletion, statistical imputation, machine learning based imputation and generative imputation. However, these methods are either incapable of dealing with temporal information or multi-stage. This paper proposes an end-to-end generative model E²GAN to impute missing values in multivariate time series. With the help of the discriminative loss and the squared error loss, E²GAN can impute the incomplete time s

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Faizin, Rahmat Nur, Mardhani Riasetiawan, and Ahmad Ashari. "A Review of Missing Sensor Data Imputation Methods." In 2019 5th International Conference on Science and Technology (ICST). IEEE, 2019. http://dx.doi.org/10.1109/icst47872.2019.9166287.

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Andrews, Justin, and Sheldon Gorell. "Generating Missing Oilfield Data Using A Generative Adversarial Imputation Network GAIN." In SPE Western Regional Meeting. SPE, 2021. http://dx.doi.org/10.2118/200766-ms.

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Abstract Missing values and incomplete observations can exist in just about ever type of recorded data. With analytical modeling, and machine learning in particular, the quantity and quality of available data is paramount to acquiring reliable results. Within the oil industry alone, priorities in which data is important can vary from company to company, leading to available knowledge of a single field to vary from place to place. With machine learning requiring very complete sets of data, this issue can require whole portions of data to be discarded in order to create an appropriate dataset. V

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Gang Chang and Tongmin Ge. "Comparison of missing data imputation methods for traffic flow." In 2011 International Conference on Transportation and Mechanical & Electrical Engineering (TMEE). IEEE, 2011. http://dx.doi.org/10.1109/tmee.2011.6199284.

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Krause, Robert W., Mark Huisman, Christian Steglich, and Tom A. B. Snijders. "Missing Network Data A Comparison of Different Imputation Methods." In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2018. http://dx.doi.org/10.1109/asonam.2018.8508716.

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Platias, Christos, and Georgios Petasis. "A Comparison of Machine Learning Methods for Data Imputation." In SETN 2020: 11th Hellenic Conference on Artificial Intelligence. ACM, 2020. http://dx.doi.org/10.1145/3411408.3411465.

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Cho, Brian, Teresa Dayrit, Yuan Gao, et al. "Effective Missing Value Imputation Methods for Building Monitoring Data." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378230.

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Reports on the topic "Imputation methods"

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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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Weller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu, and George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7600017.bard.

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The project’s general objectives were to determine specific polymorphisms at the DNA level responsible for observed quantitative trait loci (QTLs) and to estimate their effects, frequencies, and selection potential in the Holstein dairy cattle breed. The specific objectives were to (1) localize the causative polymorphisms to small chromosomal segments based on analysis of 52 U.S. Holstein bulls each with at least 100 sons with high-reliability genetic evaluations using the a posteriori granddaughter design; (2) sequence the complete genomes of at least 40 of those bulls to 20 coverage; (3) de

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