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Artículos de revistas sobre el tema "Mean error"

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Publicado: 4 de junio de 2021
Última modificación: 3 de febrero de 2022

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1

Chai, T. y R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, n.º 1 (28 de febrero de 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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2

Lee, Dominic y Carey Priebe. "Exact mean and mean squared error of the smoothed bootstrap mean integrated squared error estimator". Computational Statistics 15, n.º 2 (julio de 2000): 169–81. http://dx.doi.org/10.1007/s001800000026.

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3

Bar-Lev, Shaul K., Benzion Boukai y Peter Enis. "On the mean squared error, the mean absolute error and the like". Communications in Statistics - Theory and Methods 28, n.º 8 (enero de 1999): 1813–22. http://dx.doi.org/10.1080/03610929908832390.

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4

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

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5

Piegorsch, Walter W. y A. John Bailer. "Minimum mean-square error quadrature". Journal of Statistical Computation and Simulation 46, n.º 3-4 (mayo de 1993): 217–34. http://dx.doi.org/10.1080/00949659308811504.

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6

Tarter, Michael E. "Mean Integrated Squared Error Sampling". Journal of the American Statistical Association 81, n.º 393 (marzo de 1986): 234–42. http://dx.doi.org/10.1080/01621459.1986.10478266.

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7

Marron, J. S. y M. P. Wand. "Exact Mean Integrated Squared Error". Annals of Statistics 20, n.º 2 (junio de 1992): 712–36. http://dx.doi.org/10.1214/aos/1176348653.

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8

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

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9

Duan, Zhenyun. "MEAN ERROR EFFECT OF GEAR INTEGRATED ERROR MEASURING PROCESS". Chinese Journal of Mechanical Engineering 37, n.º 02 (2001): 55. http://dx.doi.org/10.3901/jme.2001.02.055.

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10

Ohno, Shuichi, Teruyuki Shiraki, M. Rizwan Tariq y Masaaki Nagahara. "Mean Squared Error Analysis of Quantizers With Error Feedback". IEEE Transactions on Signal Processing 65, n.º 22 (15 de noviembre de 2017): 5970–81. http://dx.doi.org/10.1109/tsp.2017.2745450.

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11

TATEISHI, Ryutaro y Chengang WEN. "Relationshio between Root Mean Square Error and Probable Error." Journal of the Japan society of photogrammetry and remote sensing 33, n.º 4 (1994): 15–22. http://dx.doi.org/10.4287/jsprs.33.4_15.

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12

Monhor, D. "The arithmetic-geometric mean and the elliptic mean error". Acta Geodaetica et Geophysica Hungarica 38, n.º 1 (febrero de 2003): 53–60. http://dx.doi.org/10.1556/ageod.38.2003.1.8.

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13

Figueroa-López, José E. y Cecilia Mancini. "Optimum thresholding using mean and conditional mean squared error". Journal of Econometrics 208, n.º 1 (enero de 2019): 179–210. http://dx.doi.org/10.1016/j.jeconom.2018.09.011.

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14

Garg, Pankaj Kumar y Debajyoti Mohanty. "Mean (Standard Deviation) or Mean (Standard Error of Mean): Time to Ponder". World Journal of Surgery 37, n.º 4 (30 de noviembre de 2012): 932. http://dx.doi.org/10.1007/s00268-012-1854-z.

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15

Yatracos, Yannis G. "On prediction and mean squared error". Canadian Journal of Statistics 20, n.º 2 (junio de 1992): 187–200. http://dx.doi.org/10.2307/3315467.

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16

Rao, J. N. K., Jiming Jiang y Kalyan Das. "Mean squared error of empirical predictor". Annals of Statistics 32, n.º 2 (abril de 2004): 818–40. http://dx.doi.org/10.1214/009053604000000201.

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17

Schmidt, David A., Michael Joham y Wolfgang Utschick. "Minimum mean square error vector precoding". European Transactions on Telecommunications 19, n.º 3 (2008): 219–31. http://dx.doi.org/10.1002/ett.1192.

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18

Song, Wheyming Tina y Bruce W. Schmeiser. "Optimal Mean-Squared-Error Batch Sizes". Management Science 41, n.º 1 (enero de 1995): 110–23. http://dx.doi.org/10.1287/mnsc.41.1.110.

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19

Rougier, Jonathan. "Ensemble Averaging and Mean Squared Error". Journal of Climate 29, n.º 24 (23 de noviembre de 2016): 8865–70. http://dx.doi.org/10.1175/jcli-d-16-0012.1.

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Abstract In fields such as climate science, it is common to compile an ensemble of different simulators for the same underlying process. It is a striking observation that the ensemble mean often outperforms at least half of the ensemble members in mean squared error (measured with respect to observations). In fact, as demonstrated in the most recent IPCC report, the ensemble mean often outperforms all or almost all of the ensemble members across a range of climate variables. This paper shows that these could be mathematical results based on convexity and averaging but with implications for the properties of the current generation of climate simulators.
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20

Beheshti, Soosan, Masoud Hashemi, Ervin Sejdic y Tom Chau. "Mean Square Error Estimation in Thresholding". IEEE Signal Processing Letters 18, n.º 2 (febrero de 2011): 103–6. http://dx.doi.org/10.1109/lsp.2010.2097590.

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21

Fišerová, Eva y Martin Kubala. "Mean fluorescence lifetime and its error". Journal of Luminescence 132, n.º 8 (agosto de 2012): 2059–64. http://dx.doi.org/10.1016/j.jlumin.2012.03.038.

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22

Chatfield, Chris. "Apples, oranges and mean square error". International Journal of Forecasting 4, n.º 4 (enero de 1988): 515–18. http://dx.doi.org/10.1016/0169-2070(88)90127-6.

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23

Cao, R. "Bootstrapping the Mean Integrated Squared Error". Journal of Multivariate Analysis 45, n.º 1 (abril de 1993): 137–60. http://dx.doi.org/10.1006/jmva.1993.1030.

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24

Frías-Paredes, Laura, Fermin Mallor, Martín Gastón-Romeo y Teresa León. "Dynamic mean absolute error as new measure for assessing forecasting errors". Energy Conversion and Management 162 (abril de 2018): 176–88. http://dx.doi.org/10.1016/j.enconman.2018.02.030.

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25

Salimov, R. F., I. N. Volodin y N. F. Nasibullina. "Sequential d-guaranteed estimate of the normal mean with bounded relative error". Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki 161, n.º 1 (2019): 145–51. http://dx.doi.org/10.26907/2541-7746.2019.1.145-151.

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26

Torabi, Mahmoud y Jon N. K. Rao. "Mean squared error estimators of small area means using survey weights". Canadian Journal of Statistics 38, n.º 4 (19 de octubre de 2010): 598–608. http://dx.doi.org/10.1002/cjs.10078.

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27

Magnussen, S., G. Frazer y M. Penner. "Alternative mean-squared error estimators for synthetic estimators of domain means". Journal of Applied Statistics 43, n.º 14 (17 de febrero de 2016): 2550–73. http://dx.doi.org/10.1080/02664763.2016.1142942.

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28

Guo, Meixi y Malay Ghosh. "Mean squared error of James–Stein estimators for measurement error models". Statistics & Probability Letters 82, n.º 11 (noviembre de 2012): 2033–43. http://dx.doi.org/10.1016/j.spl.2012.06.019.

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29

Correll, Michael y Michael Gleicher. "Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error". IEEE Transactions on Visualization and Computer Graphics 20, n.º 12 (31 de diciembre de 2014): 2142–51. http://dx.doi.org/10.1109/tvcg.2014.2346298.

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30

Chai, T. y R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature". Geoscientific Model Development 7, n.º 3 (30 de junio de 2014): 1247–50. http://dx.doi.org/10.5194/gmd-7-1247-2014.

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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. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. 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, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.
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31

Magnussen, Steen. "A New Mean Squared Error Estimator for a Synthetic Domain Mean". Forest Science 63, n.º 1 (17 de febrero de 2017): 1–9. http://dx.doi.org/10.5849/forsci.16-056.

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32

Simaan, Majeed, Yusif Simaan y Yi Tang. "Estimation error in mean returns and the mean-variance efficient frontier". International Review of Economics & Finance 56 (julio de 2018): 109–24. http://dx.doi.org/10.1016/j.iref.2017.10.019.

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33

Yonghe, Deng. "Perfectively Deducing Bessel Mean Square Error Formula". Open Civil Engineering Journal 9, n.º 1 (31 de julio de 2015): 423–25. http://dx.doi.org/10.2174/1874149501509010423.

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In the survey teaching materials of China, deducing Bessel mean square error formula is all based on survey values with same mathematical expectation. These methods aren’t perfect. So, based on survey values without same mathematics expectation to prove Bessel mean square error formula is very necessary. Therefore, considering different mathematical expectation, it is meaningful that this paper has perfectively deduced Bessel mean square error formula.
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34

Roll, Richard. "A Mean/Variance Analysis of Tracking Error". Journal of Portfolio Management 18, n.º 4 (31 de julio de 1992): 13–22. http://dx.doi.org/10.3905/jpm.1992.701922.

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35

Li, Rui y Saralees Nadarajah. "Mean and variance of round off error". Signal Processing 127 (octubre de 2016): 185–90. http://dx.doi.org/10.1016/j.sigpro.2016.03.007.

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36

de Myttenaere, Arnaud, Boris Golden, Bénédicte Le Grand y Fabrice Rossi. "Mean Absolute Percentage Error for regression models". Neurocomputing 192 (junio de 2016): 38–48. http://dx.doi.org/10.1016/j.neucom.2015.12.114.

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37

Lopez-Valcarce, R. "Realizable minimum mean-squared error channel shorteners". IEEE Transactions on Signal Processing 53, n.º 11 (noviembre de 2005): 4354–62. http://dx.doi.org/10.1109/tsp.2005.857050.

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38

Petra, Nicola, Davide De Caro, Valeria Garofalo, Ettore Napoli y Antonio G. M. Strollo. "Truncated squarer with minimum mean-square error". Microelectronics Journal 45, n.º 6 (junio de 2014): 799–804. http://dx.doi.org/10.1016/j.mejo.2014.02.018.

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39

Abel, J. S. "A bound on mean-square-estimate error". IEEE Transactions on Information Theory 39, n.º 5 (1993): 1675–80. http://dx.doi.org/10.1109/18.259655.

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40

Jianjun, Zhu. "Robust estimate with minimum mean squared error". Australian Surveyor 36, n.º 2 (junio de 1991): 111–15. http://dx.doi.org/10.1080/00050326.1991.10438723.

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41

Stoughton, Roland y Stewart Strait. "Source imaging with minimum mean‐squared error". Journal of the Acoustical Society of America 94, n.º 2 (agosto de 1993): 827–34. http://dx.doi.org/10.1121/1.408184.

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42

KOBAYASHI, Kazuo. "Two Dissimilar Semantics of Mean Square Error." Journal of the Japan society of photogrammetry and remote sensing 30, n.º 3 (1991): 42–48. http://dx.doi.org/10.4287/jsprs.30.3_42.

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43

Yokota, Yasunari y Motoki Shiga. "An entropy estimator improving mean squared error". Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 87, n.º 9 (2004): 1–10. http://dx.doi.org/10.1002/ecjc.10163.

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44

Lee, Ming Ha y Michael B. C. Khoo. "The Synthetic Mean Square Error Control Chart". Communications in Statistics - Simulation and Computation 43, n.º 6 (2 de diciembre de 2013): 1523–42. http://dx.doi.org/10.1080/03610918.2012.735321.

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45

Fuleky, Peter y Luigi Ventura. "Mean lag in general error correction models". Economics Letters 143 (junio de 2016): 107–10. http://dx.doi.org/10.1016/j.econlet.2016.03.028.

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46

-T. Leung, J. Y., T. W. Tam, C. S. Wong y G. H. Young. "Minimizing Mean Flow Time with Error Constraint". Algorithmica 20, n.º 1 (enero de 1998): 101–18. http://dx.doi.org/10.1007/pl00009185.

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47

Salman, Mohammad Shukri, Osman Kukrer y Aykut Hocanin. "Recursive inverse algorithm: Mean-square-error analysis". Digital Signal Processing 66 (julio de 2017): 10–17. http://dx.doi.org/10.1016/j.dsp.2017.04.001.

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48

Ljung, Lennart y Pierre Priouret. "Remarks on the mean square tracking error". International Journal of Adaptive Control and Signal Processing 5, n.º 6 (noviembre de 1991): 395–403. http://dx.doi.org/10.1002/acs.4480050605.

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49

Wang, Weijie y Yanmin Lu. "Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model". IOP Conference Series: Materials Science and Engineering 324 (marzo de 2018): 012049. http://dx.doi.org/10.1088/1757-899x/324/1/012049.

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50

Dalton, Lori A. y Edward R. Dougherty. "Optimal mean-square-error calibration of classifier error estimators under Bayesian models". Pattern Recognition 45, n.º 6 (junio de 2012): 2308–20. http://dx.doi.org/10.1016/j.patcog.2011.12.003.

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