Relevant bibliographies by topics / Mean error

Contents

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

Academic literature on the topic 'Mean error'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Mean error"

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Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (February 28, 2014): 1525–34. http://dx.doi.org/10.5194/gmdd-7-1525-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.
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Lee, Dominic, and Carey Priebe. "Exact mean and mean squared error of the smoothed bootstrap mean integrated squared error estimator." Computational Statistics 15, no. 2 (July 2000): 169–81. http://dx.doi.org/10.1007/s001800000026.

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Bar-Lev, Shaul K., Benzion Boukai, and Peter Enis. "On the mean squared error, the mean absolute error and the like." Communications in Statistics - Theory and Methods 28, no. 8 (January 1999): 1813–22. http://dx.doi.org/10.1080/03610929908832390.

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Khair, Ummul, Hasanul Fahmi, Sarudin Al Hakim, and Robbi Rahim. "Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error." Journal of Physics: Conference Series 930 (December 2017): 012002. http://dx.doi.org/10.1088/1742-6596/930/1/012002.

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Piegorsch, Walter W., and A. John Bailer. "Minimum mean-square error quadrature." Journal of Statistical Computation and Simulation 46, no. 3-4 (May 1993): 217–34. http://dx.doi.org/10.1080/00949659308811504.

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Tarter, Michael E. "Mean Integrated Squared Error Sampling." Journal of the American Statistical Association 81, no. 393 (March 1986): 234–42. http://dx.doi.org/10.1080/01621459.1986.10478266.

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Marron, J. S., and M. P. Wand. "Exact Mean Integrated Squared Error." Annals of Statistics 20, no. 2 (June 1992): 712–36. http://dx.doi.org/10.1214/aos/1176348653.

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Sedgwick, P. "Standard error of the mean." BMJ 340, mar17 1 (March 17, 2010): c1437. http://dx.doi.org/10.1136/bmj.c1437.

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Duan, Zhenyun. "MEAN ERROR EFFECT OF GEAR INTEGRATED ERROR MEASURING PROCESS." Chinese Journal of Mechanical Engineering 37, no. 02 (2001): 55. http://dx.doi.org/10.3901/jme.2001.02.055.

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Ohno, Shuichi, Teruyuki Shiraki, M. Rizwan Tariq, and Masaaki Nagahara. "Mean Squared Error Analysis of Quantizers With Error Feedback." IEEE Transactions on Signal Processing 65, no. 22 (November 15, 2017): 5970–81. http://dx.doi.org/10.1109/tsp.2017.2745450.

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Dissertations / Theses on the topic "Mean error"

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Degtyarena, Anna Semenovna. "The window least mean square error algorithm." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2385.

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In order to improve the performance of LMS (least mean square) algorithm by decreasing the amount of calculations this research proposes to make an update on each step only for those elements from the input data set, that fall within a small window W near the separating hyperplane surface. This work aims to describe in detail the results that can be achieved by using the proposed LMS with window learning algorithm in information systems that employ the methodology of neural network for the purposes of classification.
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Cui, Xiangchen. "Mean-Square Error Bounds and Perfect Sampling for Conditional Coding." DigitalCommons@USU, 2000. https://digitalcommons.usu.edu/etd/7107.

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In this dissertation, new theoretical results are obtained for bounding convergence and mean-square error in conditional coding. Further new statistical methods for the practical application of conditional coding are developed. Criteria for the uniform convergence are first examined. Conditional coding Markov chains are aperiodic, π-irreducible, and Harris recurrent. By applying the general theories of uniform ergodicity of Markov chains on genera l state space, one can conclude that conditional coding Markov cha ins are uniformly ergodic and further, theoretical convergence rates based on Doeblin's condition can be found. Conditional coding Markov chains can be also viewed as having finite state space. This allows use of techniques to get bounds on the second largest eigenvalue which lead to bounds on convergence rate and the mean-square error of sample averages. The results are applied in two examples showing that these bounds are useful in practice. Next some algorithms for perfect sampling in conditional coding are studied. An application of exact sampling to the independence sampler is shown to be equivalent to standard rejection sampling. In case of single-site updating, traditional perfect sampling is not directly applicable when the state space has large cardinality and is not stochastically ordered, so a new procedure is developed that gives perfect samples at a predetermined confidence interval. In last chapter procedures and possibilities of applying conditional coding to mixture models are explored. Conditional coding can be used for analysis of a finite mixture model. This methodology is general and easy to use.
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Kong, Kar-lun, and 江嘉倫. "Some mean value theorems for certain error terms in analytic number theory." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206432.

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Strobel, Matthias. "Estimation of minimum mean squared error with variable metric from censored observations." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-35333.

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Jakobsson, Sofie. ""How mean can you be?" : A study of teacher trainee and teacher views on error correction." Thesis, Högskolan i Gävle, Akademin för utbildning och ekonomi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-8426.

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The present study investigates three teacher trainees and three teachers’ views on error correction during oral communication, and the similarities and differences between them. These six people were interviewed separately and they were asked six questions; the first five questions were asked to all six people but the last question differed between the teacher trainees and the teachers. My result shows that the teacher trainees are insecure when it comes to error correction and that the teachers´ sees it as a part of their job, and that is the biggest difference between them. The teacher trainees and the teachers focus on the same types of errors and those are the errors that can cause problems in communication, and that can be pronunciation errors, grammatical errors or vocabulary errors.
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Dear, K. B. G. "A generalisation of mean squared error and its application to variance component estimation." Thesis, University of Reading, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379691.

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Potter, Chris. "Modeling Channel Estimation Error in Continuously Varying MIMO Channels." International Foundation for Telemetering, 2007. http://hdl.handle.net/10150/604490.

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ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada
The accuracy of channel estimation plays a crucial role in the demodulation of data symbols sent across an unknown wireless medium. In this work a new analytical expression for the channel estimation error of a multiple input multiple output (MIMO) system is obtained when the wireless medium is continuously changing in the temporal domain. Numerical examples are provided to illustrate our findings.
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Nazar, Gabriel Luca. "Fine-grained error detection techniques for fast repair of FPGAs." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/77746.

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Field Programmable Gate Arrays (FPGAs) são componentes reconfiguráveis de hardware que encontraram grande sucesso comercial ao longo dos últimos anos em uma grande variedade de nichos de aplicação. Alta vazão de processamento, flexibilidade e tempo de projeto reduzido estão entre os principais atrativos desses dispositivos, e são essenciais para o seu sucesso comercial. Essas propriedades também são valiosas para sistemas críticos, que frequentemente enfrentam restrições severas de desempenho. Além disso, a possibilidade de reprogramação após implantação é relevante, uma vez que permite a adição de novas funcionalidades ou a correção de erros de projeto, estendendo a vida útil do sistema. Tais dispositivos, entretanto, dependem de grandes memórias para armazenar o bitstream de configuração, responsável por definir a função presente do FPGA. Assim, falhas afetando esta configuração são capazes de causar defeitos funcionais, sendo uma grande ameaça à confiabilidade. A forma mais tradicional de remover tais erros, isto é, scrubbing de configuração, consiste em periodicamente sobrescrever a memória com o seu conteúdo desejado. Entretanto, devido ao seu tamanho significativo e à banda de acesso limitada, scrubbing sofre de um longo tempo médio de reparo, e que está aumentando à medida que FPGAs ficam maiores e mais complexos a cada geração. Partições reconfiguráveis são úteis para reduzir este tempo, já que permitem a execução de um procedimento local de reparo na partição afetada. Para este propósito, mecanismos rápidos de detecção de erros são necessários para rapidamente disparar este scrubbing localizado e reduzir a latência de erro. Além disso, diagnóstico preciso é necessário para identificar a localização do erro dentro do espaço de endereçamento da configuração. Técnicas de redundância de grão fino têm o potencial de prover ambos, mas normalmente introduzem custos significativos devido à necessidade de numerosos verificadores de redundância. Neste trabalho, propomos uma técnica de detecção de erros de grão fino que utiliza recursos abundantes e subutilizados encontrados em FPGAs do estado da arte, especificamente as cadeias de propagação de vai-um. Assim, a técnica provê os principais benefícios da redundância de grão fino enquanto minimiza sua principal desvantagem. Reduções bastante significativas na latência de erro são atingíveis com a técnica proposta. Também é proposto um mecanismo heurístico para explorar o diagnóstico provido por técnicas desta natureza. Este mecanismo tem por objetivo identificar as localizações mais prováveis do erro na memória de configuração, baseado no diagnóstico de grão fino, e fazer uso dessa informação de forma a minimizar o tempo de reparo.
Field Programmable Gate Arrays (FPGAs) are reconfigurable hardware components that have found great commercial success over the past years in a wide variety of application niches. High processing throughput, flexibility and reduced design time are among the main assets of such devices, and are essential to their commercial success. These features are also valuable for critical systems that often face stringent performance constraints. Furthermore, the possibility to perform post-deployment reprogramming is relevant, as it allows adding new functionalities or correcting design mistakes, extending the system lifetime. Such devices, however, rely on large memories to store the configuration bitstream, responsible for defining the current FPGA function. Thus, faults affecting this configuration are able to cause functional failures, posing a major dependability threat. The most traditional means to remove such errors, i.e., configuration scrubbing, consists in periodically overwriting the memory with its desired contents. However, due to its significant size and limited access bandwidth, scrubbing suffers from a long mean time to repair, and which is increasing as FPGAs get larger and more complex after each generation. Reconfigurable partitions are useful to reduce this time, as they allow performing a local repair procedure on the affected partition. For that purpose, fast error detection mechanisms are required, in order to quickly trigger this localized scrubbing and reduce error latency. Moreover, precise diagnosis is necessary to identify the error location within the configuration addressing space. Fine-grained redundancy techniques have the potential to provide both, but usually introduce significant costs due to the need of numerous redundancy checkers. In this work we propose a fine-grained error detection technique that makes use of abundant and underused resources found in state-of-the-art FPGAs, namely the carry propagation chains. Thereby, the technique provides the main benefits of fine-grained redundancy while minimizing its main drawback. Very significant reductions in error latency are attainable with the proposed approach. A heuristic mechanism to explore the diagnosis provided by techniques of this nature is also proposed. This mechanism aims at identifying the most likely error locations in the configuration memory, based on the fine-grained diagnosis, and to make use of this information in order to minimize the repair time of scrubbing.
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林啓任 and Kai-yam Lam. "Some results on the mean values of certain error terms in analytic number theory." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31214241.

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Lam, Kai-yam. "Some results on the mean values of certain error terms in analytic number theory /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18611977.

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Books on the topic "Mean error"

1

Clements, Michael P. On the limitations of comparing mean square forecast error. Oxford: Oxford University, Institute of Economics and Statistics, 1992.

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Baram, Yoram. Mean-square error bounds for reduced-order linear state estimators. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.

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Hoque, Asraul. The exact multiperiod mean-square forecast error for the first-order autoregressive model. London: London School of Economics, 1986.

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Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive modelwith an intercept. London: National Institute of Economic and Social Research, 1988.

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Mean justice: A town's terror, a prosecutor's power, a betrayal of innocence. New York: Simon & Schuster, 1999.

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Magnus, Jan R. The exact multiperiod mean-square forecast error for the first-order autoregressive model with an intercept. London: International Centre for Economics and Related Disciplines, 1988.

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Davis, James Arthur. Peak-to-mean power control and error correction for OFDM transmission using Golay sequences and Reed-Muller codes. Palo Alto, CA: Hewlett-Packard Laboratories, Technical Publications Department, 1996.

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Gaver, Donald Paul. Bayesian prediction of mean square errors with covariates. Monterey, Calif: Naval Postgraduate School, 1992.

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Um-- slips, stumbles, and verbal blunders, and what they mean. New York: Pantheon Books, 2007.

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Erard, Michael. Um: Slips, stumbles, and verbal blunders, and what they mean. New York, NY: Pantheon Books, 2008.

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Book chapters on the topic "Mean error"

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Mean Error." In Encyclopedia of Machine Learning, 652. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_526.

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Mean Absolute Error." In Encyclopedia of Machine Learning, 652. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_525.

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Mean Squared Error." In Encyclopedia of Machine Learning, 653. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_528.

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Shekhar, Shashi, and Hui Xiong. "Root-Mean-Square Error." In Encyclopedia of GIS, 979. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1142.

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Benesty, Jacob, Jingdong Chen, Yiteng Huang, and Israel Cohen. "Mean-Squared Error Criterion." In Noise Reduction in Speech Processing, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00296-0_4.

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Franzen, Michael. "Standard Error of the Mean." In Encyclopedia of Clinical Neuropsychology, 3278. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1248.

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Dekking, Frederik Michel, Cornelis Kraaikamp, Hendrik Paul Lopuhaä, and Ludolf Erwin Meester. "Efficiency and mean squared error." In A Modern Introduction to Probability and Statistics, 299–311. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-168-7_20.

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Cicchetti, Domenic V. "Standard Error of the Mean." In Encyclopedia of Autism Spectrum Disorders, 2976–77. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_323.

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Franzen, Michael D. "Standard Error of the Mean." In Encyclopedia of Clinical Neuropsychology, 2367–68. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1248.

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Weik, Martin H. "minimum mean-square error filtering." In Computer Science and Communications Dictionary, 1022. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_11569.

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Conference papers on the topic "Mean error"

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Chakravorty, Suman, Pierre Kabamba, and David Hyland. "Optimal Mean Squared Error Imaging." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-4952.

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Schmidt, David A., Changxin Shi, Randall A. Berry, Michael L. Honig, and Wolfgang Utschick. "Minimum Mean Squared Error interference alignment." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5470055.

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Loce, Robert P., and Edward R. Dougherty. "Morphological filter mean-absolute-error theorem." In SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, edited by Edward R. Dougherty, Jaakko T. Astola, and Charles G. Boncelet, Jr. SPIE, 1992. http://dx.doi.org/10.1117/12.58363.

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Xue, Wufeng, Xuanqin Mou, Lei Zhang, and Xiangchu Feng. "Perceptual Fidelity Aware Mean Squared Error." In 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.93.

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Gusi-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.

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Ephraim, Y. "On minimum mean square error speech enhancement." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150509.

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Weimer, James, Nicola Bezzo, Miroslav Pajic, Oleg Sokolsky, and Insup Lee. "Attack-resilient minimum mean-squared error estimation." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859478.

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Peng, Siyuan, Zongze Wu, and Badong Chen. "Constrained least mean p-power error algorithm." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554155.

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Loce, Robert P., and Edward R. Dougherty. "Mean-absolute-error theorem for computational morphology." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Edward R. Dougherty, Paul D. Gader, and Jean C. Serra. SPIE, 1993. http://dx.doi.org/10.1117/12.146680.

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William, Peter E., and Michael W. Hoffman. "Error Entropy and Mean Square Error Minimization for Lossless Image Compression." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312813.

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Reports on the topic "Mean error"

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Zhao, L. C. Exponential Bounds of Mean Error for the Kernal Estimates of Regression Functions. Fort Belvoir, VA: Defense Technical Information Center, December 1985. http://dx.doi.org/10.21236/ada167345.

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Lio, Y. L., and W. J. Padgett. On the Mean Squared Error of Nonparametric Quantile Estimators under Random Right-Censorship. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada174517.

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Zhao, L. C. Exponential Bounds of Mean Error for the Nearest Neighbor Estimates of Regression Functions. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada166156.

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Sun, Winston Y. Linear adaptive noise-reduction filters for tomographic imaging: Optimizing for minimum mean square error. Office of Scientific and Technical Information (OSTI), April 1993. http://dx.doi.org/10.2172/10148667.

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Chen, X. R., and L. C. Zhoa. Necessary and Sufficient Conditions for the Convergence of Integrated and Mean-Integrated r-th Order Error of Histogram Density Estimates. Fort Belvoir, VA: Defense Technical Information Center, April 1987. http://dx.doi.org/10.21236/ada186037.

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Gaver, Donald P., and Patricia A. Jacobs. Bayesian Prediction of Mean Square Errors with Covariates. Fort Belvoir, VA: Defense Technical Information Center, November 1992. http://dx.doi.org/10.21236/ada259585.

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Collins, Clarence O., and Tyler J. Hesser. altWIZ : A System for Satellite Radar Altimeter Evaluation of Modeled Wave Heights. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39699.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the design and implementation of a wave model evaluation system, altWIZ, which uses wave height observations from operational satellite radar altimeters. The altWIZ system utilizes two recently released altimeter databases: Ribal and Young (2019) and European Space Agency Sea State Climate Change Initiative v.1.1 level 2 (Dodet et al. 2020). The system facilitates model evaluation against 1 Hz1 altimeter data or a product created by averaging altimeter data in space and time around model grid points. The system allows, for the first time, quantitative analysis of spatial model errors within the U.S. Army Corps of Engineers (USACE) Wave Information Study (WIS) 30+ year hindcast for coastal United States. The system is demonstrated on the WIS 2017 Atlantic hindcast, using a 1/2° basin scale grid and a 1/4° regional grid of the East Coast. Consistent spatial patterns of increased bias and root-mean-square-error are exposed. Seasonal strengthening and weakening of these spatial patterns are found, related to the seasonal variation of wave energy. Some model errors correspond to areas known for high currents, and thus wave-current interaction. In conjunction with the model comparison, additional functions for pairing altimeter measurements with buoy data and storm tracks have been built. Appendices give information on the code access (Appendix I), organization and files (Appendix II), example usage (Appendix III), and demonstrating options (Appendix IV).
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Clark, Todd, and Kenneth West. Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference. Cambridge, MA: National Bureau of Economic Research, January 2005. http://dx.doi.org/10.3386/t0305.

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Bruder, Brittany L., Katherine L. Brodie, Tyler J. Hesser, Nicholas J. Spore, Matthew W. Farthing, and Alexander D. Renaud. guiBath y : A Graphical User Interface to Estimate Nearshore Bathymetry from Hovering Unmanned Aerial System Imagery. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39700.

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This US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, technical report details guiBathy, a graphical user interface to estimate nearshore bathymetry from imagery collected via a hovering Unmanned Aerial System (UAS). guiBathy provides an end-to-end solution for non-subject-matter-experts to utilize commercia-off-the-shelf UAS to collect quantitative imagery of the nearshore by packaging robust photogrammetric and signal-processing algorithms into an easy-to-use software interface. This report begins by providing brief background on coastal imaging and the photogrammetry and bathymetric inversion algorithms guiBathy utilizes, as well as UAS data collection requirements. The report then describes guiBathy software specifications, features, and workflow. Example guiBathy applications conclude the report with UAS bathymetry measurements taken during the 2020 Atlantic Hurricane Season, which compare favorably (root mean square error = 0.44 to 0.72 m; bias = -0.35 to -0.11 m) with in situ survey measurements. guiBathy is a standalone executable software for Windows 10 platforms and will be freely available at www.github.com/erdc.
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10

Leis, Sherry. Vegetation community monitoring at Lincoln Boyhood National Memorial: 2011–2019. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2284711.

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Lincoln Boyhood National Memorial celebrates the lives of the Lincoln family including the final resting place of Abraham’s mother, Nancy Hanks Lincoln. Lincoln’s childhood in Indiana was a formative time in the life our 16th president. When the Lincoln family arrived in Indiana, the property was covered in the oak-hickory forest type. They cleared land to create their homestead and farm. Later, designers of the memorial felt that it was important to restore woodlands to the site. The woodlands would help visitors visualize the challenges the Lincoln family faced in establishing and maintaining their homestead. Some stands of woodland may have remained, but significant restoration efforts included extensive tree planting. The Heartland Inventory and Monitoring Network began monitoring the woodland in 2011 with repeat visits every four years. These monitoring efforts provide a window into the composition and structure of the wood-lands. We measure both overstory trees and the ground flora within four permanently located plots. At these permanent plots, we record each species, foliar cover estimates of ground flora, diameter at breast height of midstory and overstory trees, and tree regeneration frequency (tree seedlings and saplings). The forest species composition was relatively consistent over the three monitoring events. Climatic conditions measured by the Palmer Drought Severity Index indicated mild to wet conditions over the monitoring record. Canopy closure continued to indicate a forest structure with a closed canopy. Large trees (>45 cm DBH) comprised the greatest amount of tree basal area. Sugar maple was observed to have the greatest basal area and density of the 23 tree species observed. The oaks characteristic of the early woodlands were present, but less dominant. Although one hickory species was present, it was in very low abundance. Of the 17 tree species recorded in the regeneration layer, three species were most abundant through time: sugar maple (Acer saccharum), red bud (Cercis canadensis), and ash (Fraxinus sp.). Ash recruitment seemed to increase over prior years and maple saplings transitioned to larger size classes. Ground flora diversity was similar through time, but alpha and gamma diversity were slightly greater in 2019. Percent cover by plant guild varied through time with native woody plants and forbs having the greatest abundance. Nonnative plants were also an important part of the ground flora composition. Common periwinkle (Vinca minor) and Japanese honeysuckle (Lonicera japonica) continued to be the most abundant nonnative species, but these two species were less abundant in 2019 than 2011. Unvegetated ground cover was high (mean = 95%) and increased by 17% since 2011. Bare ground increased from less than 1% in 2011 to 9% in 2019, but other ground cover elements were similar to prior years. In 2019, we quantified observer error by double sampling two plots within three of the monitoring sites. We found total pseudoturnover to be about 29% (i.e., 29% of the species records differed between observers due to observer error). This 29% pseudoturnover rate was almost 50% greater than our goal of 20% pseudoturnover. The majority of the error was attributed to observers overlooking species. Plot frame relocation error likely contributed as well but we were unable to separate it from overlooking error with our design.
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