Добірка наукової літератури з теми "Missing Value Imputation"
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Статті в журналах з теми "Missing Value Imputation":
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 (June 28, 2022): 9199–207. http://dx.doi.org/10.1609/aaai.v36i8.20906.
Lu, Kaifeng. "Number of imputations needed to stabilize estimated treatment difference in longitudinal data analysis." Statistical Methods in Medical Research 26, no. 2 (October 10, 2014): 674–90. http://dx.doi.org/10.1177/0962280214554439.
Hameed, Wafaa Mustafa, and Nzar A. Ali. "Missing value imputation Techniques: A Survey." UHD Journal of Science and Technology 7, no. 1 (March 28, 2023): 72–81. http://dx.doi.org/10.21928/uhdjst.v7n1y2023.pp72-81.
Das, Dipalika, Maya Nayak, and Subhendu Kumar Pani. "Missing Value Imputation-A Review." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 548–58. http://dx.doi.org/10.26438/ijcse/v7i4.548558.
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 (May 31, 2022): 278. http://dx.doi.org/10.30630/joiv.6.1-2.935.
Huang, Min-Wei, Wei-Chao Lin, and Chih-Fong Tsai. "Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets." Journal of Healthcare Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/1817479.
Kumar, Nishith, Md Aminul Hoque, Md Shahjaman, S. M. Shahinul Islam, and Md Nurul Haque Mollah. "A New Approach of Outlier-robust Missing Value Imputation for Metabolomics Data Analysis." Current Bioinformatics 14, no. 1 (December 6, 2018): 43–52. http://dx.doi.org/10.2174/1574893612666171121154655.
Zimmermann, Pavel, Petr Mazouch, and Klára Hulíková Tesárková. "Missing Categorical Data Imputation and Individual Observation Level Imputation." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 62, no. 6 (2014): 1527–34. http://dx.doi.org/10.11118/actaun201462061527.
H.Mohamed, Marghny, Abdel-Rahiem A. Hashem, and Mohammed M. Abdelsamea. "Scalable Algorithms for Missing Value Imputation." International Journal of Computer Applications 87, no. 11 (February 14, 2014): 35–42. http://dx.doi.org/10.5120/15255-4019.
Gashler, Michael S, Michael R Smith, Richard Morris, and Tony Martinez. "Missing Value Imputation with Unsupervised Backpropagation." Computational Intelligence 32, no. 2 (July 1, 2014): 196–215. http://dx.doi.org/10.1111/coin.12048.
Дисертації з теми "Missing Value Imputation":
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.
Andersson, Joacim, and Henrik Falk. "Missing Data in Value-at-Risk Analysis : Conditional Imputation in Optimal Portfolios Using Regression." Thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-122276.
Bischof, Stefan, Andreas Harth, Benedikt Kämpgen, Axel Polleres, and Patrik Schneider. "Enriching integrated statistical open city data by combining equational knowledge and missing value imputation." Elsevier, 2017. http://dx.doi.org/10.1016/j.websem.2017.09.003.
Jagirdar, Suresh. "Investigation into Regression Analysis of Multivariate Additional Value and Missing Value Data Models Using Artificial Neural Networks and Imputation Techniques." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1219343139.
Bala, Abdalla. "Impact analysis of a multiple imputation technique for handling missing value in the ISBSG repository of software projects." Mémoire, École de technologie supérieure, 2013. http://espace.etsmtl.ca/1236/1/BALA_Abdalla.pdf.
Etourneau, Lucas. "Contrôle du FDR et imputation de valeurs manquantes pour l'analyse de données de protéomiques par spectrométrie de masse." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALS001.
Proteomics involves characterizing the proteome of a biological sample, that is, the set of proteins it contains, and doing so as exhaustively as possible. By identifying and quantifying protein fragments that are analyzable by mass spectrometry (known as peptides), proteomics provides access to the level of gene expression at a given moment. This is crucial information for improving the understanding of molecular mechanisms at play within living organisms. These experiments produce large amounts of data, often complex to interpret and subject to various biases. They require reliable data processing methods that ensure a certain level of quality control, as to guarantee the relevance of the resulting biological conclusions.The work of this thesis focuses on improving this data processing, and specifically on the following two major points:The first is controlling for the false discovery rate (FDR), when either identifying (1) peptides or (2) quantitatively differential biomarkers between a tested biological condition and its negative control. Our contributions focus on establishing links between the empirical methods stemmed for proteomic practice and other theoretically supported methods. This notably allows us to provide directions for the improvement of FDR control methods used for peptide identification.The second point focuses on managing missing values, which are often numerous and complex in nature, making them impossible to ignore. Specifically, we have developed a new algorithm for imputing them that leverages the specificities of proteomics data. Our algorithm has been tested and compared to other methods on multiple datasets and according to various metrics, and it generally achieves the best performance. Moreover, it is the first algorithm that allows imputation following the trending paradigm of "multi-omics": if it is relevant to the experiment, it can impute more reliably by relying on transcriptomic information, which quantifies the level of messenger RNA expression present in the sample. Finally, Pirat is implemented in a freely available software package, making it easy to use for the proteomic community
Gheyas, Iffat A. "Novel computationally intelligent machine learning algorithms for data mining and knowledge discovery." Thesis, University of Stirling, 2009. http://hdl.handle.net/1893/2152.
Alarcon, Sergio Arciniegas. "Imputação de dados em experimentos multiambientais: novos algoritmos utilizando a decomposição por valores singulares." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-10052016-130506/.
The biplot analysis using the additive main effects and multiplicative interaction models (AMMI) require complete data matrix, but often multi-environments trials have missing values. This thesis proposed new methods of single and multiple imputation that can be used to analyze unbalanced data in experiments with genotype by environment interaction (G×E). The first is a new extension of the cross-validation method by eigenvector (Bro et al., 2008). The second, corresponds to a new non-parametric algorithm obtained through modifications of the simple imputation method developed by Yan (2013). Also is included a study that considers imputation systems recently reported in the literature and compares them with the classic procedure recommended for imputation in trials (G×E), it means, the combination of the Expectation-Maximization (EM) algorithm with the additive main effects and multiplicative interaction (AMMI) model or EM-AMMI. Finally, are supplied generalizations of simple imputation described by Arciniegas-Alarcón et al. (2010) that combines regression with lower-rank approximation of a matrix. All methodologies are based on singular value decomposition (SVD), so, are free of any distributional or structural assumptions. In order to determine the performance of the new imputation schemes were performed simulations based on real data set of different species, with values deleted randomly at different percentages and the quality of the imputations was evaluated using different statistics. It was concluded that SVD provides a useful and flexible tool for the construction of efficient techniques that circumvent the problem of missing data in experimental matrices.
Bengtsson, Fanny, and Klara Lindblad. "Methods for handling missing values : A simulation study comparing imputation methods for missing values on a Poisson distributed explanatory variable." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432467.
Huo, Zhao. "A Comparsion of Multiple Imputation Methods for Missing Covariate Values in Recurrent Event Data." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256602.
Книги з теми "Missing Value Imputation":
Templ, Matthias. Visualization and Imputation of Missing Values. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30073-8.
Subramanian, Rajesh. Transitioning to multiple imputation: A new method to impute missing blood alcohol concentration (BAC) values in FARS. Washington, D.C: National Highway Traffic Safety Administration, National Center for Statistics and Analysis, 2002.
Missing Value Imputation. India: Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630756.
Частини книг з теми "Missing Value Imputation":
Raja, P. S., and K. Thangavel. "Soft Clustering Based Missing Value Imputation." In Digital Connectivity – Social Impact, 119–33. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3274-5_10.
Manna, Sweta, and Soumen Kumar Pati. "Missing Value Imputation Using Correlation Coefficient." In Computational Intelligence in Pattern Recognition, 551–58. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2449-3_47.
Sujatha, M., G. Lavanya Devi, K. Srinivasa Rao, and N. Ramesh. "Rough Set Theory Based Missing Value Imputation." In Cognitive Science and Health Bioinformatics, 97–106. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6653-5_9.
Shi, Yi, Zhipeng Cai, and Guohui Lin. "Classification Accuracy Based Microarray Missing Value Imputation." In Bioinformatics Algorithms, 303–27. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470253441.ch14.
Rashid, Wajeeha, and Manoj Kumar Gupta. "A Perspective of Missing Value Imputation Approaches." In Advances in Intelligent Systems and Computing, 307–15. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1275-9_25.
Rashid, Wajeeha, Sakshi Arora, and Manoj Kumar Gupta. "Missing Value Imputation Approach Using Cosine Similarity Measure." In Advances in Intelligent Systems and Computing, 557–65. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5113-0_44.
Wu, Jiahua, Xiangyan Tang, Guangxing Liu, and Bofan Wu. "An Overview of Graph Data Missing Value Imputation." In Communications in Computer and Information Science, 256–70. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1280-9_20.
Gond, Vikesh Kumar, Aditya Dubey, Akhtar Rasool, and Nilay Khare. "Missing Value Imputation Using Weighted KNN and Genetic Algorithm." In ICT Analysis and Applications, 161–69. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5224-1_18.
Cheng, Yu, Lan Wang, and Jinglu Hu. "A Quasi-linear Approach for Microarray Missing Value Imputation." In Neural Information Processing, 233–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24955-6_28.
Singh, Ninni, Anum Javeed, Sheenu Chhabra, and Pardeep Kumar. "Missing Value Imputation with Unsupervised Kohonen Self Organizing Map." In Emerging Research in Computing, Information, Communication and Applications, 61–76. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2550-8_7.
Тези доповідей конференцій з теми "Missing Value Imputation":
Luo, Fei, Hangwei Qian, Di Wang, Xu Guo, Yan Sun, Eng Sing Lee, Hui Hwang Teong, Ray Tian Rui Lai, and Chunyan Miao. "Missing Value Imputation for Diabetes Prediction." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892398.
Chong He, Hui-Hui Li, Changbo Zhao, Guo-Zheng Li, and Wei Zhang. "Triple imputation for microarray missing value estimation." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359682.
Karanikola, Aikaterini, and Sotiris Kotsiantis. "A hybrid method for missing value imputation." In PCI '19: 23rd Pan-Hellenic Conference on Informatics. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3368640.3368653.
Aidos, Helena, and Pedro Tomas. "Neighborhood-aware autoencoder for missing value imputation." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287580.
Rachmawan, Irene Erlyn Wina, and Ali Ridho Barakbah. "Optimization of missing value imputation using Reinforcement Programming." In 2015 International Electronics Symposium (IES). IEEE, 2015. http://dx.doi.org/10.1109/elecsym.2015.7380828.
Lee, Namgil. "Block Tensor Train Decomposition for Missing Value Imputation." In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2018. http://dx.doi.org/10.23919/apsipa.2018.8659560.
Li, Hui-Hui, Feng-Feng Shao, and Guo-Zheng Li. "Semi-supervised imputation for microarray missing value estimation." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999172.
Zhang, Chengqi, Yongsong Qin, Xiaofeng Zhu, Jilian Zhang, and Shichao Zhang. "Clustering-based Missing Value Imputation for Data Preprocessing." In 2006 IEEE International Conference on Industrial Informatics. IEEE, 2006. http://dx.doi.org/10.1109/indin.2006.275767.
Xu, Zhen, and Sargur N. Srihari. "Missing value imputation: with application to handwriting data." In IS&T/SPIE Electronic Imaging, edited by Eric K. Ringger and Bart Lamiroy. SPIE, 2015. http://dx.doi.org/10.1117/12.2075842.
Bou, Savong, Toshiyuki Amagasa, Hiroyuki Kitagawa, Salman Ahmed Shaikh, and Akiyoshi Matono. "Efficient Missing Value Imputation by Maximum Distance Likelihood." In 2023 IEEE International Conference on Big Data (BigData). IEEE, 2023. http://dx.doi.org/10.1109/bigdata59044.2023.10386584.
Звіти організацій з теми "Missing Value Imputation":
Sukasih, Amang S., and Victoria Scott. Cyclical Tree-Based Hot Deck Imputation. RTI Press, June 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0052.2307.
Kott, Phillip S. The Role of Weights in Regression Modeling and Imputation. RTI Press, April 2022. http://dx.doi.org/10.3768/rtipress.2022.mr.0047.2203.
Huang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Deng Hong-Wen, and Zhang Chaoyang. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), February 2024. http://dx.doi.org/10.21079/11681/48221.
Cao, Honggao. IMPUTE: A SAS Application System for Missing Value Imputations--With Special Reference to HRS Income/Assets. Institute for Social Research, University of Michigan, 2001. http://dx.doi.org/10.7826/isr-um.06.585031.001.05.0006.2001.