Results 221 to 230 of about 296,811 (276)
Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China. [PDF]
Yang J, Pan J, Chen J, Xu Y, Zhang X.
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Test, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kazuhiro Ohtani
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kazuhiro Ohtani
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Communications in Statistics - Simulation and Computation, 2019
This paper is concerned with using the E-Bayesian method for computing estimates of Pareto index.
Ming Han
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This paper is concerned with using the E-Bayesian method for computing estimates of Pareto index.
Ming Han
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Journal of Geodesy, 2007
In a linear Gauss–Markov model, the parameter estimates from BLUUE (Best Linear Uniformly Unbiased Estimate) are not robust against possible outliers in the observations. Moreover, by giving up the unbiasedness constraint, the mean squared error (MSE) risk may be further reduced, in particular when the problem is ill-posed.
Burkhard Schaffrin, Schaffrin Burkhard
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In a linear Gauss–Markov model, the parameter estimates from BLUUE (Best Linear Uniformly Unbiased Estimate) are not robust against possible outliers in the observations. Moreover, by giving up the unbiasedness constraint, the mean squared error (MSE) risk may be further reduced, in particular when the problem is ill-posed.
Burkhard Schaffrin, Schaffrin Burkhard
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The Mean Square Error (MSE) Performance Criteria
1986Adaptive signal processing algorithms generally attempt to optimize a performance measure that is a function of the unknown parameters to be identified. The most pervasive of these performance measures are based upon squared prediction errors, although the specific prediction error used in adaptation often depends upon the particular algorithm.
S. Thomas Alexander
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