Optical Fiber Vibration Sensor Using Least Mean Square Error Algorithm [PDF]
Sensors, 2020 In order to enhance the signal-to-noise ratio (SNR) of a distributed optical fiber vibration sensor based on coherent optical time domain reflectometry (COTDR), a high extinction ratio cascade structure of an acousto-optic modulator and semiconductor ...
Yu Wang +6 more
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Optimum Thresholding Using Mean and Conditional Mean Square Error [PDF]
SSRN Electronic Journal, 2017 We consider a univariate semimartingale model for (the logarithm of) an asset price, containing jumps having possibly infinite activity (IA). The nonparametric threshold estimator of the integrated variance IV proposed in Mancini 2009 is constructed using observations on a discrete time grid, and precisely it sums up the squared increments of the ...
MANCINI, CECILIA, Figueroa Lopez, Jose
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Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature [PDF]
Geoscientific Model Development, 2014 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
T. Chai, R. R. Draxler
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Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not [PDF]
Geoscientific Model Development, 2022 The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide ...
T. O. Hodson
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Mean Squared Error, Deconstructed [PDF]
Journal of Advances in Modeling Earth Systems, 2021 AbstractAs science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model
Timothy O. Hodson +2 more
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Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model. [PDF]
Pattern Recognit, 2014 Zollanvari A, Dougherty ER.
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A novel extended Gumbel Type II model with statistical inference and Covid-19 applications
Results in Physics, 2022 Statistical models play an important role in data analysis, and statisticians are constantly looking for new or relatively new statistical models to fit data sets across a wide range of fields.
Showkat Ahmad Lone +3 more
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Bayesian minimum mean square error for transmissivity sensing
Physical Review Research, 2023 We address the problem of estimating the transmissivity of the pure-loss single-mode bosonic channel from the Bayesian point of view, i.e., when a prior probability distribution function (PDF) on the transmissivity is available.
Boyu Zhou +3 more
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Theoretical Structure and Applications of a Newly Enhanced Gumbel Type II Model
Mathematics, 2023 Statistical models are vital in data analysis, and researchers are always on the search for potential or the latest statistical models to fit data sets in a variety of domains. To create an improved statistical model, we used a T-X transformation and the
Showkat Ahmad Lone +5 more
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On the mean square error of randomized averaging algorithms [PDF]
, 2013 This paper regards randomized discrete-time consensus systems that preserve the average "on average". As a main result, we provide an upper bound on the mean square deviation of the consensus value from the initial average.
Frasca, Paolo, Hendrickx, Julien M.
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