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Stable approach based diagonal recurrent quantum neural networks for identification of nonlinear systems. [PDF]

Sci Rep
Khalil H, Elshazly O, Shaheen O.
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Bootstrapping Error Component Models

Computational Statistics, 2001
The one-way error component model \(y_{it}=\alpha+x_{it}\beta+\mu_i+\varepsilon_{it}\), \(i=1,\dots,n\), \(t=1,\dots,T\), is considered, where \(y_{it}\) and \(x_{it}\) are observable variables, \(\mu_i\sim(0,\sigma_\mu^2)\), \(\varepsilon_{it}\sim(0,\sigma_\varepsilon^2)\), \(E\varepsilon_{it},\varepsilon_{js}=0\), \(E\mu_i\mu_j=0\) and \(E\mu_i ...
Andersson, Michael K., Karlsson, Sune
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Flexible Parametric Measurement Error Models

Biometrics, 1999
Summary.Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect, the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric inference, we propose using flexible parametric models that can accommodate departures from ...
Carroll, Raymond J., Roeder, Kathryn, Wasserman, Larry   +2 more
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Statistical model of systematic errors: linear error model

Chemometrics and Intelligent Laboratory Systems, 1996
Abstract A statistical model of systematic errors when processing the results of a few experiments is presented. The model includes the shift and tilt systematic errors (the linear error model). To obtain estimates of unknown parameters and unknown variance components, the maximum likelihood method is suggested to apply.
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Statistical model error

Kidney International, 2023
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