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In this paper, a fixed design regression model where the errors follow a strictly stationary process is considered.
We construct efficient estimators of the identifiable parameters in a regression model when the errors follow a stationary parametric ARCH(P) process.
11.11pm BST First set: Williams* 4-0 Errani (*denotes next server) Errani error opens and two Williams errors follow – Errani is showing a little spark here but you'd think Williams is her own worst, and perhaps only real enemy here.
By adopting the generalised variational assimilation method, which assumes that errors follow a non-Gaussian distribution, this paper assimilates the cloud-affected brightness temperature using simulated data for the hyper-spectral atmospheric infrared sounder (AIRS).
In the most of previous data reconciliation DR) studies, process data were conventionally characterized by normal Gaussian distribution, so the optimality/validity of DR estimator is implicitly based on a main assumption that errors follow normal Gaussian distribution.
However, we assume that the errors follow an exponential power distribution.
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We also found that recovery from errors follows a long-tailed distribution with an exponential cut-off.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com