Results 1 to 10 of about 613,752 (314)
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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Journal of Agricultural Engineering, 2019 Estimation of reference evapotranspiration (ET0) with the Food and Agricultural Organisation (FAO) Penman-Monteith model requires temperature, relative humidity, solar radiation, and wind speed data.
Homayoon Ganji, Takamitsu Kajisa
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On the Performance of Principal Component Liu-Type Estimator under the Mean Square Error Criterion [PDF]
Journal of Applied Mathematics, 2013 Wu (2013) proposed an estimator, principal component Liu-type estimator, to overcome multicollinearity. This estimator is a general estimator which includes ordinary least squares estimator, principal component regression estimator, ridge estimator, Liu ...
Jibo Wu
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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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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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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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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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Sensors, 2023 This paper discusses the results of simulations relating to the performances of turbo codes, low density parity check (LDPC) codes, and polar codes over an additive white Gaussian noise (AWGN) channel in the presence of inter symbol interference ...
Adriana-Maria Cuc +2 more
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