Academic literature on the topic 'Median-Of-Mean'
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Journal articles on the topic "Median-Of-Mean"
Griffin, Lewis D. "Mean, median and mode filtering of images." Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 456, no. 2004 (December 8, 2000): 2995–3004. http://dx.doi.org/10.1098/rspa.2000.0650.
Full textHoseana, Jonathan, and Franco Vivaldi. "Geometrical properties of the mean-median map." Journal of Computational Dynamics 7, no. 1 (2020): 83–121. http://dx.doi.org/10.3934/jcd.2020004.
Full textBakker, Arthur, and Koeno P. E. Gravemeijer. "An Historical Phenomenology of Mean and Median." Educational Studies in Mathematics 62, no. 2 (June 2006): 149–68. http://dx.doi.org/10.1007/s10649-006-7099-8.
Full textKaur, Amanpreet, and Ravneet Kaur Sidhu. "Edge Preservation of Enhanced Fuzzy Median Mean Filter Using Decision Based Median Filter." International Journal on Soft Computing 7, no. 1 (February 29, 2016): 17–26. http://dx.doi.org/10.5121/ijsc.2016.7102.
Full textTeng, Guangqiang, Yanpeng Li, Boping Tian, and Jie Li. "Sharper Concentration Inequalities for Median-of-Mean Processes." Mathematics 11, no. 17 (August 30, 2023): 3730. http://dx.doi.org/10.3390/math11173730.
Full textYusuf, Ajibola Yahya, Ahmed Audu, and Mojeed Abiodun Yunusa. "POWER MEDIAN-BASED ESTIMATORS OF FINITE POPULATION MEAN." FUDMA JOURNAL OF SCIENCES 8, no. 2 (April 30, 2024): 296–300. http://dx.doi.org/10.33003/fjs-2024-0802-2291.
Full textSarkar, Jyotirmoy, and Mamunur Rashid. "Visualizing Mean, Median, Mean Deviation, and Standard Deviation of a Set of Numbers." American Statistician 70, no. 3 (July 2, 2016): 304–12. http://dx.doi.org/10.1080/00031305.2016.1165734.
Full textSmith, Woollcott, and Milton Parnes. "Mean Streets: The Median of a Size-Biased Sample and the Population Mean." American Statistician 48, no. 2 (May 1994): 106. http://dx.doi.org/10.2307/2684257.
Full textSmith, Woollcott, and Milton Parnes. "Mean Streets: The Median of a Size-Biased Sample and the Population Mean." American Statistician 48, no. 2 (May 1994): 106–10. http://dx.doi.org/10.1080/00031305.1994.10476034.
Full textBasu, S., and A. DasGupta. "The Mean, Median, and Mode of Unimodal Distributions:A Characterization." Theory of Probability & Its Applications 41, no. 2 (January 1997): 210–23. http://dx.doi.org/10.1137/s0040585x97975447.
Full textDissertations / Theses on the topic "Median-Of-Mean"
Mathieu, Timothée. "M-estimation and Median of Means applied to statistical learning Robust classification via MOM minimization MONK – outlier-robust mean embedding estimation by median-of-means Excess risk bounds in robust empirical risk minimization." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASM002.
Full textThe main objective of this thesis is to study methods for robust statistical learning. Traditionally, in statistics we use models or simplifying assumptions that allow us to represent the real world. However, some deviations from the hypotheses can strongly disrupt the statistical analysis of a database. By robust statistics, we mean methods that can handle on the one hand so-called abnormal data (sensor error, human error) but also data of a highly variable nature. We apply robust techniques to statistical learning, giving theoretical efficiency results of the proposed methods as well as illustrations on simulated and real data
Kwon, Yongung. "EFFECTS OF CHRONIC ANKLE INSTABILITY AND REST INTERVALS ON PERFORMANCE, NEUROMUSCULAR FUNCTION, POSTURAL CONTROL, AND BIOMECHANICS DURING THE STAR EXCURSION BALANCE TEST." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/4054.
Full textSmékal, Ondřej. "Příjmová nerovnost mužů a žen v ČR s přihlédnutím k vybraným zemím EU." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-205067.
Full textDepersin, Jules. "Statistical and Computational Complexities of Robust and High-Dimensional Estimation Problems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAG009.
Full textStatistical learning theory aims at providing a better understanding of the statistical properties of learning algorithms. These properties are often derived assuming the underlying data are gathered by sampling independent and identically distributed gaussian (or subgaussian) random variables. These properties can thus be drastically affected by the presence of gross errors (also called "outliers") in the data, and by data being heavy-tailed. We are interested in procedures that have good properties even when part of the data is corrupted and heavy-tailed, procedures that we call extit{robusts}, that we often get in this thesis by using the Median-Of-Mean heuristic.We are especially interested in procedures that are robust in high-dimensional set-ups, and we study (i) how dimensionality affects the statistical properties of robust procedures, and (ii) how dimensionality affects the computational complexity of the associated algorithms. In the study of the statistical properties (i), we find that for a large range of problems, the statistical complexity of the problems and its "robustness" can be in a sense "decoupled", leading to bounds where the dimension-dependent term is added to the term that depends on the corruption, rather than multiplied by it. We propose ways of measuring the statistical complexities of some problems in that corrupted framework, using for instance VC-dimension. We also provide lower bounds for some of those problems.In the study of computational complexity of the associated algorithm (ii), we show that in two special cases, namely robust mean-estimation with respect to the euclidean norm and robust regression, one can relax the associated optimization problems that becomes exponentially hard with the dimension to get tractable algorithm that behaves polynomially in the dimension
Cao, Jennifer Yue. "The performance and robustness of confidence intervals for the median of a symmetric distribution constructed assuming sampling from a Cauchy distribution." Kansas State University, 2012. http://hdl.handle.net/2097/14897.
Full textDepartment of Statistics
Paul Nelson
Trimmed means are robust estimators of location for distributions having heavy tails. Theory and simulation indicate that little efficiency is lost under normality when using appropriately trimmed means and that their use with data from distributions with heavy tails can result in improved performance. This report uses the principle of equivariance applied to trimmed means sampled from a Cauchy distribution to form a discrepancy function of the data and parameters whose distribution is free of the unknown median and scale parameter. Quantiles of this discrepancy function are estimated via asymptotic normality and simulation and used to construct confidence intervals for the median of a Cauchy distribution. A nonparametric approach based on the distribution of order statistics is also used to construct confidence intervals. The performance of these intervals in terms of coverage rate and average length is investigated via simulation when the data are actually sampled from a Cauchy distribution and when sampling is from normal and logistic distributions. The intervals based on simulation estimation of the quantiles of the discrepancy function are shown to perform well across a range of sample sizes and trimming proportions when the data are actually sampled from a Cauchy distribution and to be relatively robust when sampling is from the normal and logistic distributions.
Kristjánsson, Liljar Már. "Lower Limb Muscle Fatigue Analysis of RaceRunning Athletes with Cerebral Palsy." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232695.
Full textMaharaju, Rajkumar. "Devolopment of Mean and Median Based Adaptive Search Algorithm for Motion Estimation in SNR Scalable Video Coding." Thesis, 2015. http://ethesis.nitrkl.ac.in/7725/1/2015_MT_Development_Rajkumar_Maharaju.pdf.
Full textBooks on the topic "Median-Of-Mean"
Linde, Barbara M. Climates of the World: Identifying and Comparing Mean, Median, and Mode. Rosen Publishing Group, 2004.
Find full textClimates of the World: Identifying and Comparing Mean, Median, and Mode (Powermath). PowerKids Press, 2005.
Find full textLinde, Barbara M. Climates of the World: Identifying and Comparing Mean, Median, and Mode (Powermath). PowerKids Press, 2005.
Find full textLinde, Barbara M. Climates of the World: Identifying and Comparing Mean, Median, and Mode (Powermath). PowerKids Press, 2005.
Find full textAlexander, Peter D. G., and Malachy O. Columb. Presentation and handling of data, descriptive and inferential statistics. Edited by Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0028.
Full textWirba, Ebenezer Lemven, Fiennasah Annif' Akem, and Francis Menjo Baye. Scrutinizing the sticky floor/glass ceiling phenomena in the informal labour market in Cameroon: An unconditional quantile regression analysis. 13th ed. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/2021/947-1.
Full textGelman, Andrew, and Deborah Nolan. Descriptive statistics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0003.
Full textPetchey, Owen L., Andrew P. Beckerman, Natalie Cooper, and Dylan Z. Childs. Insights from Data with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198849810.001.0001.
Full textAuerbach, Charles, and Wendy Zeitlin. SSD for R. 2nd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197582756.001.0001.
Full textBook chapters on the topic "Median-Of-Mean"
Jiang, Xiaoyi, and Horst Bunke. "Weighed Mean Generalized Median of Strings." In Pattern Recognition and String Matching, 295–314. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4613-0231-5_12.
Full textBonamente, Massimiliano. "Mean, Median, and Average Values of Variables." In Statistics and Analysis of Scientific Data, 107–15. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6572-4_6.
Full textLandtblom, Karin Kihlblom. "Prospective Teachers’ Conceptions of the Concepts Mean, Median and Mode." In Students' and Teachers' Values, Attitudes, Feelings and Beliefs in Mathematics Classrooms, 43–52. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70244-5_5.
Full textFerguson, Daniel, and François G. Meyer. "On the Number of Edges of the Fréchet Mean and Median Graphs." In Network Science, 26–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97240-0_3.
Full textHellwig, Marcus. "Approximation to the Location Parameters Modal, Mean, Median; Introduction of the Sine Derivative." In Equibalancedistribution (Eqbl) in the analysis of earthquake data, 67–70. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29859-3_7.
Full textBird, John. "Revision Test 13: Presentation of statistical data, mean, median, mode, standard deviation and probability." In Basic Engineering Mathematics, 369. 7th ed. | Abingdon, Oxon ; New York, NY : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315561776-51.
Full textBird, John. "Revision Test 10 Presentation of statistical data, mean, median, mode, standard deviation and probability." In Engineering Mathematics, 409. 8th edition. | Abingdon, Oxon ; New York, NY : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315561851-58.
Full textKreinovich, Vladik, Hung T. Nguyen, and Rujira Ouncharoen. "From Mean and Median Income to the Most Adequate Way of Taking Inequality into Account." In Econometrics of Risk, 63–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13449-9_5.
Full textThongpanja, Sirinee, Angkoon Phinyomark, Chusak Limsakul, and Pomchai Phukpattaranont. "Application of Mean and Median Frequency Methods for Identification of Human Joint Angles Using EMG Signal." In Lecture Notes in Electrical Engineering, 689–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46578-3_81.
Full textSrikanth, Rangu, Koppisetti Lakshmi Sowmya, Sika Anjana, Gajarajula Vamshi, and Annem Ram Mohan Reddy. "Improved Image Enhancement of Natural Images with Median Mean-Based Sub-Image Clipped Histogram Equalization." In Advances in Intelligent Systems and Computing, 873–87. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5301-8_61.
Full textConference papers on the topic "Median-Of-Mean"
Jain, Brijnesh, and Klaus Obermayer. "Consistent Estimator of Median and Mean Graph." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.258.
Full textGusi-Amigo, Adria, Pau Ciosas, and Luc Vandendorpe. "Mean square error performance of sample mean and sample median estimators." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551739.
Full textJacobbe, Tim. "Elementary school teachers’ understanding of the mean and median." In Joint ICMI/IASE Study: Teaching Statistics in School Mathematics. International Association for Statistical Education, 2008. http://dx.doi.org/10.52041/srap.08311.
Full textWang, Demin, Joseph Ronsin, and Veronique Haese-Coat. "Compared performances of morphological, median type, and running mean filters." In Applications in Optical Science and Engineering, edited by Petros Maragos. SPIE, 1992. http://dx.doi.org/10.1117/12.131456.
Full textMokti, Mohammad Nordin, and Rosalina Abdul Salam. "Hybrid of Mean-shift and median-cut algorithm for fish segmentation." In 2008 International Conference on Electronic Design (ICED 2008). IEEE, 2008. http://dx.doi.org/10.1109/iced.2008.4786645.
Full textDeshpande, Suyog D., Meng H. Er, Ronda Venkateswarlu, and Philip Chan. "Max-mean and max-median filters for detection of small targets." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Oliver E. Drummond. SPIE, 1999. http://dx.doi.org/10.1117/12.364049.
Full textSharma, Shachi, and Pranay Yadav. "Removal of fixed valued impulse noise by improved Trimmed Mean Median filter." In 2014 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2014. http://dx.doi.org/10.1109/iccic.2014.7238368.
Full textCahyadi, B. N., Wan Khairunizam, M. Nor Muhammad, I. Zunaidi, S. H. Majid, M. N. Rudzuan, S. A. Bakar, Z. M. Razlan, and W. A. Mustafa. "Analysis of EMG based Arm Movement Sequence using Mean and Median Frequency." In 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2018. http://dx.doi.org/10.1109/eecsi.2018.8752777.
Full textDonghui Li. "On asymptotic properties for the median point of Cauchy Mean-value Theorem." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6002502.
Full textKoyuncu, Nursel. "Improved ratio estimation of population mean under median and neoteric ranked set sampling." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992297.
Full textReports on the topic "Median-Of-Mean"
Garciga, Christian L., Randal J. Verbrugge, and Saeed Zaman. The Effect of Component Disaggregation on Measures of the Median and Trimmed-Mean CPI. Federal Reserve Bank of Cleveland, January 2024. http://dx.doi.org/10.26509/frbc-wp-202402.
Full textAnderson, Andrew, and Mark Yacucci. Inventory and Statistical Characterization of Inorganic Soil Constituents in Illinois. Illinois Center for Transportation, June 2021. http://dx.doi.org/10.36501/0197-9191/21-006.
Full textAnderson, Andrew, and Mark Yacucci. Inventory and Statistical Characterization of Inorganic Soil Constituents in Illinois: Appendices. Illinois Center for Transportation, June 2021. http://dx.doi.org/10.36501/0197-9191/21-007.
Full textKim, Joseph, and Patricia McCarthy. Evaluation of Sustainability Determinants to Develop a Sustainability Rating System for California Infrastructure Construction Projects. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.2142.
Full textPeterson, Brian, J. Beeco, Sharolyn Anderson, and Damon Joyce. Exploring spatial patterns of overflights at Mount Rushmore National Memorial. National Park Service, June 2022. http://dx.doi.org/10.36967/nrr-2293663.
Full textRaymond, Kara, Laura Palacios, Cheryl McIntyre, and Evan Gwilliam. Status of climate and water resources at Saguaro National Park: Water year 2019. Edited by Alice Wondrak Biel. National Park Service, December 2021. http://dx.doi.org/10.36967/nrr-2288717.
Full textDatta, Sandip, and Geeta Gandhi Kingdon. The Myth and Reality of Teacher Shortage in India: An Investigation Using 2019-20 Data. Research on Improving Systems of Education (RISE), December 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2020/072.
Full textRine, Kristin, Roger Christopherson, and Jason Ransom. Harlequin duck (Histrionicus histrionicus) occurrence and habitat selection in North Cascades National Park Service Complex, Washington. National Park Service, April 2022. http://dx.doi.org/10.36967/nrr-2293127.
Full textMazzoni, Silvia, Nicholas Gregor, Linda Al Atik, Yousef Bozorgnia, David Welch, and Gregory Deierlein. Probabilistic Seismic Hazard Analysis and Selecting and Scaling of Ground-Motion Records (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/zjdn7385.
Full textThe space between: Analysis of gender and ethnicity pay gaps in UK-based organisations active in global health. Global Health 50/50, November 2023. http://dx.doi.org/10.56649/zhpp4836.
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