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With imprecise error estimates, the step-size adjustment has to be conservative to preserve accuracy, or it risks producing an imprecise result.
Sometimes, estimating the step response h (t) of the system is also considered deconvolution.
In this paper, backtracking line search, which is also known as the Armijo rule [36], is used to estimate the step size.
Doğu and Kocakoc (2013) estimated the step change point of multivariate normal processes when joint mean vector and covariance matrix shifts occurred.
As the main contribution in this paper, we propose a modular methodology based on artificial neural networks for estimating the step changes in the covariance matrix of multivariate-attribute processes.
The main purpose is to estimate the stepping-stone effect separately for natives and immigrants, where the latter group potentially benefits the most from TWA employment.
Following a signal from the proposed multi-attribute control chart, the MLEs are applied to estimate the step-change and linear-trend disturbance points.
The first two steps examine various time series properties of the data to identify the proper model, the third step estimates the model, and the fourth step validates it.
The prediction step estimates the object trajectories at the current time step according to a dynamic model for interacting objects.
A pedometer estimates the number of steps taken based on acceleration signals.
Once this equation has been fitted and parameters estimated, the next step is to fit submodel relating field BA and ρ to TCH.
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