Relevant bibliographies by topics / Outliers

Contents

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

Academic literature on the topic 'Outliers'

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

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Journal articles on the topic "Outliers"

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Seo, Han Son. "Outlier tests on potential outliers." Korean Journal of Applied Statistics 30, no. 1 (2017): 159–67. http://dx.doi.org/10.5351/kjas.2017.30.1.159.

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Huda, Nur'ainul Miftahul, Utriweni Mukhaiyar, and Nurfitri Imro'ah. "AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 3 (2022): 975–84. http://dx.doi.org/10.30598/barekengvol16iss3pp975-984.

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Outliers are observations that differ significantly from others that can affect the estimation results in the model and reduce the estimator's accuracy. To deal with outliers is to remove outliers from the data. However, sometimes important information is contained in the outlier, so eliminating outliers is a misinterpretation. There are two types of outliers in the time series model, Innovative Outlier (IO) and Additive Outlier (AO). In the GSTAR model, outliers and spatial and time correlations can also be detected. We introduce an iterative procedure for detecting outliers in the GSTAR mode
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., Srividya, S. Mohanavalli, N. Sripriya, and S. Poornima. "Outlier Detection using Clustering Techniques." International Journal of Engineering & Technology 7, no. 3.12 (2018): 813. http://dx.doi.org/10.14419/ijet.v7i3.12.16508.

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An outlier is nothing but a pattern that is different compared to the other existing patterns in a particular dataset. In some applications it is very important to understand and identify outliers. Detecting outlier is of major importance in many of the fields like cybersecurity, machine learning, finance, healthcare, etc., A clustering based method is proposed to detect outliers using different algorithms like k means, PAM, Clara, DBScan and LOF on different data sets like breast cancer, heart diseases, multi shaped datasets. This work aims to identify the best suitable method to detect the o
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Agyemang, Malik, Ken Barker, and Reda Alhajj. "Web outlier mining: Discovering outliers from web datasets1." Intelligent Data Analysis 9, no. 5 (2005): 473–86. http://dx.doi.org/10.3233/ida-2005-9505.

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Muhima, Rani Rotul, Muchamad Kurniawan, and Oktavian Tegar Pambudi. "A LOF K-Means Clustering on Hotspot Data." International Journal of Artificial Intelligence & Robotics (IJAIR) 2, no. 1 (2020): 29. http://dx.doi.org/10.25139/ijair.v2i1.2634.

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K-Means is the most popular of clustering method, but its drawback is sensitivity to outliers. This paper discusses the addition of the outlier removal method to the K-Means method to improve the performance of clustering. The outlier removal method was added to the Local Outlier Factor (LOF). LOF is the representative outlier’s detection algorithm based on density. In this research, the method is called LOF K-Means. The first applying clustering by using the K-Means method on hotspot data and then finding outliers using the LOF method. The object detected outliers are then removed. Then new c
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Mishra, Deepti, and Devpriya Soni. "Outliers in Data Mining: Approaches and Detection." International Journal of Engineering & Technology 7, no. 4.39 (2018): 189–98. http://dx.doi.org/10.14419/ijet.v7i4.39.23930.

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The paper is grounded on the study of outliers which are the objects that somehow arise unlike from residue data stored and can be pointed as outliers. At present in data mining Outlier detection is the currently innovative topic for research. Outliers detection in a set of patterns is a pertinent problem in the data mining area. Outlier mining is the problem of detecting unseen events, abnormal data and exceptions. Another perspective of outliers they affect the outcomes and analysis of data. Presence of outliers make the results in confusable state. The patterns generated after the calculati
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Syed Abd Mutalib, Sharifah Sakinah, Siti Zanariah Satari, and Wan Nur Syahidah Wan Yusoff. "SYNTHETIC MULTIVARIATE DATA GENERATION PROCEDURE WITH VARIOUS OUTLIER SCENARIOS USING R PROGRAMMING LANGUAGE." Jurnal Teknologi 84, no. 3 (2022): 89–101. http://dx.doi.org/10.11113/jurnalteknologi.v84.17900.

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A synthetic data generation procedure is a procedure to generate data from either a statistical or mathematical model. The data generation procedure has been used in simulation studies to compare statistical performance methods or propose a new statistical method with a specific distribution. A synthetic multivariate data generation procedure with various outlier scenarios using R is formulated in this study. An outlier generating model is used to generate multivariate data that contains outliers. Data generation procedures for various outlier scenarios by using R are explained. Three outlier
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Lartey, Clement, Jixue Liu, Richmond K. Asamoah, Christopher Greet, Massimiliano Zanin, and William Skinner. "Effective Outlier Detection for Ensuring Data Quality in Flotation Data Modelling Using Machine Learning (ML) Algorithms." Minerals 14, no. 9 (2024): 925. http://dx.doi.org/10.3390/min14090925.

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Froth flotation, a widely used mineral beneficiation technique, generates substantial volumes of data, offering the opportunity to extract valuable insights from these data for production line analysis. The quality of flotation data is critical to designing accurate prediction models and process optimisation. Unfortunately, industrial flotation data are often compromised by quality issues such as outliers that can produce misleading or erroneous analytical results. A general approach is to preprocess the data by replacing or imputing outliers with data values that have no connection with the r
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Parrinello, Christina M., Morgan E. Grams, Yingying Sang, et al. "Iterative Outlier Removal: A Method for Identifying Outliers in Laboratory Recalibration Studies." Clinical Chemistry 62, no. 7 (2016): 966–72. http://dx.doi.org/10.1373/clinchem.2016.255216.

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Abstract BACKGROUND Extreme values that arise for any reason, including those through nonlaboratory measurement procedure-related processes (inadequate mixing, evaporation, mislabeling), lead to outliers and inflate errors in recalibration studies. We present an approach termed iterative outlier removal (IOR) for identifying such outliers. METHODS We previously identified substantial laboratory drift in uric acid measurements in the Atherosclerosis Risk in Communities (ARIC) Study over time. Serum uric acid was originally measured in 1990–1992 on a Coulter DACOS instrument using an uricase-bas
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Yulistiani, Selma, and Suliadi Suliadi. "Deteksi Pencilan pada Model ARIMA dengan Bayesian Information Criterion (BIC) Termodifikasi." STATISTIKA: Journal of Theoretical Statistics and Its Applications 19, no. 1 (2019): 29–37. http://dx.doi.org/10.29313/jstat.v19i1.4740.

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Time series data may be affected by special events or circumstances such as promotions, natural disasters, etc. These events can lead to inconsistent observations in the series called outliers. Because outliers can make invalid conclusions, it is important to carry out procedures in detecting outlier effects. In outlier detection there is one type of outlier, namely additive outlier (AO). The process of detecting additive outliers in the ARIMA model can be said as a model selection problem, where the candidate model assumes additive outliers at a certain time. In the selection of models there
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Dissertations / Theses on the topic "Outliers"

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Sean, Viseth. "Exploration Framework For Detecting Outliers In Data Streams." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/395.

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Current real-world applications are generating a large volume of datasets that are often continuously updated over time. Detecting outliers on such evolving datasets requires us to continuously update the result. Furthermore, the response time is very important for these time critical applications. This is challenging. First, the algorithm is complex; even mining outliers from a static dataset once is already very expensive. Second, users need to specify input parameters to approach the true outliers. While the number of parameters is large, using a trial and error approach online would be not
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Beau, Thabiso. "Normality of JSE Returns: Macro-outliers, Micro-outliers: an Empirical Evaluation." Master's thesis, Faculty of Commerce, 2019. https://hdl.handle.net/11427/31721.

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Previous work on the empirical distribution of security returns has found that equity returns are not normally distributed. These findings have brought the applicability of certain asset allocation and pricing frameworks into question. This study examines whether the removal of a priori macro-outliers and micro-outliers leads to improved fits to the Gaussian distribution for single-listed equities on the Johannesburg Stock Exchange (JSE). Single-listed equities refer to stocks (i) listed on the JSE Main Board over the period covered in this study, (ii) that comprise of the exchange’s largest 1
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Mitchell, Napoleon. "Outliers and Regression Models." Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc279029/.

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The mitigation of outliers serves to increase the strength of a relationship between variables. This study defined outliers in three different ways and used five regression procedures to describe the effects of outliers on 50 data sets. This study also examined the relationship among the shape of the distribution, skewness, and outliers.
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Yin, Yong. "Outliers in Time Series /." Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262638388.

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Halldestam, Markus. "ANOVA - The Effect of Outliers." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295864.

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This bachelor’s thesis focuses on the effect of outliers on the one-way analysis of variance and examines whether the estimate in ANOVA is robust and whether the actual test itself is robust from influence of extreme outliers. The robustness of the estimates is examined using the breakdown point while the robustness of the test is examined by simulating the hypothesis test under some extreme situations. This study finds evidence that the estimates in ANOVA are sensitive to outliers, i.e. that the procedure is not robust. Samples with a larger portion of extreme outliers have a higher type-I er
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Schall, Robert. "Outliers and influence under arbitrary variance." Doctoral thesis, University of Cape Town, 1986. http://hdl.handle.net/11427/21913.

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Using a geometric approach to best linear unbiased estimation in the general linear model, the additional sum of squares principle, used to generate decompositions, can be generalized allowing for an efficient treatment of augmented linear models. The notion of the admissibility of a new variable is useful in augmenting models. Best linear unbiased estimation and tests of hypotheses can be performed through transformations and reparametrizations of the general linear model. The theory of outliers and influential observations can be generalized so as to be applicable for the general univariate
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Campos, Guilherme Oliveira. "Estudo, avaliação e comparação de técnicas de detecção não supervisionada de outliers." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-04082015-084412/.

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A área de detecção de outliers (ou detecção de anomalias) possui um papel fundamental na descoberta de padrões em dados que podem ser considerados excepcionais sob alguma perspectiva. Detectar tais padrões é relevante de maneira geral porque, em muitas aplicações de mineração de dados, tais padrões representam comportamentos extraordinários que merecem uma atenção especial. Uma importante distinção se dá entre as técnicas supervisionadas e não supervisionadas de detecção. O presente projeto enfoca as técnicas de detecção não supervisionadas. Existem dezenas de algoritmos desta categoria na lit
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Berton, Lilian. "Caracterização de classes e detecção de outliers em redes complexa." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-19072011-132701/.

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As redes complexas surgiram como uma nova e importante maneira de representação e abstração de dados capaz de capturar as relações espaciais, topológicas, funcionais, entre outras características presentes em muitas bases de dados. Dentre as várias abordagens para a análise de dados, destacam-se a classificação e a detecção de outliers. A classificação de dados permite atribuir uma classe aos dados, baseada nas características de seus atributos e a detecção de outliers busca por dados cujas características se diferem dos demais. Métodos de classificação de dados e de detecção de outliers basea
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Iranzo, Pérez David. "Análisis de outliers: un caso a estudio." Doctoral thesis, Universitat de València, 2007. http://hdl.handle.net/10803/9467.

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Una de las limitaciones del estudio de series temporales mediante lamodelización ARIMA, y en concreto a través del enfoque Box-Jenkins, es la dificultadde identificar correctamente el modelo y, en su caso, seleccionar el más adecuado. Elprocedimiento de filtrado estándar para estimar el ciclo de negocios puede requeriralgunas correcciones previas de las series, dado que, de otro modo, se podrían producirgraves distorsiones en los resultados. Un destacado ejemplo es la corrección por outliersque es tratada, junto con el resto de ajustes previos.Los outliers denotan observaciones atípicas que, h
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Dunagan, John D. (John David) 1976. "A geometric theory of outliers and perturbation." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8396.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2002.<br>Includes bibliographical references (p. 91-94).<br>We develop a new understanding of outliers and the behavior of linear programs under perturbation. Outliers are ubiquitous in scientific theory and practice. We analyze a simple algorithm for removal of outliers from a high-dimensional data set and show the algorithm to be asymptotically good. We extend this result to distributions that we can access only by sampling, and also to the optimization version of the problem. Our results cover both the discrete an
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Books on the topic "Outliers"

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Gladwell, Malcolm. Outliers. Little, Brown and Company, 2008.

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Toby, Lewis, ed. Outliers in statistical data. 3rd ed. Wiley, 1994.

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Gladwell, Malcolm. Outliers: The story of success. Little, Brown and Co. Large Print, 2008.

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Gladwell, Malcolm. Outliers: The story of success. Little, Brown and Co., 2008.

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Masoom Ali, Mir, Rahmatullah Imon, Irfan Ali, and Haitham M. Yousof. Statistical Outliers and Related Topics. CRC Press, 2024. http://dx.doi.org/10.1201/9781003379881.

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GOVERNMENT, US. Chacoan Outliers Protection Act of 1995. U.S. G.P.O., 1995.

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1944-, Hoaglin David C., ed. How to detect and handle outliers. ASQC Quality Press, 1993.

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Guttman, Irwin. Spuriosity and outliers in circular data. University of Toronto, Dept. of Statistics, 1988.

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Francis, Thompson. St Kilda and other Hebridean outliers. David & Charles, 1988.

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Rumburg, Scot. Characteristics of directly expanded hog data outliers. Research and Applications Division, National Agricultural Statistics Service, U.S. Department of Agriculture, 1992.

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Book chapters on the topic "Outliers"

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Baragona, Roberto, Francesco Battaglia, and Irene Poli. "Outliers." In Evolutionary Statistical Procedures. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16218-3_6.

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Barrie Wetherill, G., P. Duncombe, M. Kenward, J. Köllerström, S. R. Paul, and B. J. Vowden. "Outliers." In Regression Analysis with Applications. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4105-2_6.

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Nahler, Gerhard. "outliers." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_983.

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Krasker, William S. "Outliers." In Time Series and Statistics. Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_25.

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Liu, Yan. "Outliers." In Encyclopedia of Quality of Life and Well-Being Research. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2039.

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O’Connor, Jennifer. "Outliers." In EAI International Conference on Technology, Innovation, Entrepreneurship and Education. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16130-9_13.

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Liu, Yan. "Outliers." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_2039.

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Hexel, Vasco. "Outliers." In 50 Movie Music Moments. Routledge, 2023. http://dx.doi.org/10.4324/9781003280897-7.

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Krasker, William S. "Outliers." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_1884-1.

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Lewis, Toby. "Outliers." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_437.

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Conference papers on the topic "Outliers"

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Liu, Ninghao, Donghwa Shin, and Xia Hu. "Contextual Outlier Interpretation." 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.2018/341.

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While outlier detection has been intensively studied in many applications, interpretation is becoming increasingly important to help people trust and evaluate the developed detection models through providing intrinsic reasons why the given outliers are identified. It is a nontrivial task for interpreting the abnormality of outliers due to the distinct characteristics of different detection models, complicated structures of data in certain applications, and imbalanced distribution of outliers and normal instances. In addition, contexts where outliers locate, as well as the relation between outl
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Li, Yongmou, Yijie Wang, and Hongtao Guan. "Improve the Detection of Clustered Outliers via Outlier Score Propagation." In 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2019. http://dx.doi.org/10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00155.

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Wang, Maximilian J., Guifen Mao, and Haiquan Chen. "Mining multivariate outliers." In the 2014 ACM Southeast Regional Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2638404.2638526.

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Gupta, Manish, Jing Gao, Yizhou Sun, and Jiawei Han. "Integrating community matching and outlier detection for mining evolutionary community outliers." In the 18th ACM SIGKDD international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339667.

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Qin, Jiahang, Yongping Hou, and Liying Ma. "Research on Automatic Removal of Outliers in Fuel Cell Test Data and Fitting Method of Polarization Curve." In WCX SAE World Congress Experience. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2896.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Fuel cell vehicles have always garnered a lot of attention in terms of energy utilization and environmental protection. In the analysis of fuel cell performance, there are usually some outliers present in the raw experimental data that can significantly affect the data analysis results. Therefore, data cleaning work is necessary to remove these outliers. The polarization curve is a crucial tool for describing the basic characteristics of fuel cells, typically described by semi-empirical formulas. The parameters in these
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Sidiropoulos, Anastasios, Dingkang Wang, and Yusu Wang. "Metric embeddings with outliers." In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2017. http://dx.doi.org/10.1137/1.9781611974782.43.

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Wu, Ou, Jun Gao, Weiming Hu, Bing Li, and Mingliang Zhu. "Identifying Multi-instance Outliers." In Proceedings of the 2010 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2010. http://dx.doi.org/10.1137/1.9781611972801.38.

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Kolesárová, Anna, and Radko Mesiar. "Aggregation Based on Outliers." In 19th World Congress of the International Fuzzy Systems Association (IFSA), 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and 11th International Summer School on Aggregation Operators (AGOP). Atlantis Press, 2021. http://dx.doi.org/10.2991/asum.k.210827.078.

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Følstad, Asbjørn, Effie Lai-Chong Law, and Kasper Hornbæk. "Outliers in usability testing." In the 7th Nordic Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2399016.2399056.

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Har-Peled, Sariel, and Yusu Wang. "Shape fitting with outliers." In the nineteenth conference. ACM Press, 2003. http://dx.doi.org/10.1145/777792.777798.

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Reports on the topic "Outliers"

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Álvarez Florens Odendahl, Luis J., and Germán López-Espinosa. Data outliers and Bayesian VARs in the euro area. Banco de España, 2022. http://dx.doi.org/10.53479/23552.

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We propose a method to adjust for data outliers in Bayesian Vector Autoregressions (BVARs), which allows for different outlier magnitudes across variables and rescales the reduced form error terms. We use the method to document several facts about the effect of outliers on estimation and out-of-sample forecasting results using euro area macroeconomic data. First, the COVID-19 pandemic led to large swings in macroeconomic data that distort the BVAR estimation results. Second, these swings can be addressed by rescaling the shocks’ variance. Third, taking into account outliers before 2020 leads t
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Fenimore, Edward E. The cause of outliers in electromagnetic pulse (EMP) locations. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1159220.

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Taveras, Elsie, Richard Marshall, Mona Sharifi, et al. Improving Childhood Obesity Outcomes: Testing Best Practices of Positive Outliers. Patient-Centered Outcomes Research Institute (PCORI), 2018. http://dx.doi.org/10.25302/3.2018.ih.13046739.

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Sadler, Brian M., and Stephen D. Casey. On Periodic Pulse Interval Analysis with Outliers and Missing Observations. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada454910.

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Eidsvik, Jo, and Steinar L. Ellefmo. Fast detection of outliers and anomalies in joint frequency data. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0025.

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Mustard, P. S., and G. E. Rouse. Sedimentary Outliers of the eastern Georgia Basin Margin, British Columbia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/132517.

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Giltinan, D. M., R. J. Carroll, and D. Ruppert. Some New Estimation Methods for Weighted Regression When There are Possible Outliers. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada152104.

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Lucon, Enrico. Statistical Detection of Outliers in the Certification of NIST Reference Charpy Lots. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ir.8526.

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Taplin, Ross, and Adrian E. Raftery. Analysis of Agricultural Field Trials in the Presence of Outliers and Fertility Jumps. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada242454.

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Mathew, Jijo K., Christopher M. Day, Howell Li, and Darcy M. Bullock. Curating Automatic Vehicle Location Data to Compare the Performance of Outlier Filtering Methods. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317435.

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Agencies use a variety of technologies and data providers to obtain travel time information. The best quality data can be obtained from second-by-second tracking of vehicles, but that data presents many challenges in terms of privacy, storage requirements and analysis. More frequently agencies collect or purchase segment travel time based upon some type of matching of vehicles between two spatially distributed points. Typical methods for that data collection involve license plate re-identification, Bluetooth, Wi-Fi, or some type of rolling DSRC identifier. One of the challenges in each of thes
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