Sentence examples for model order variance from inspiring English sources

Finally, a brief numerical comparison with closed loop experiment design based on a high model order variance expression is presented.

This result has been derived under several circumstances, first using asymptotic (in model order) variance expressions but also more recently for ARMAX models of finite order.

For example, for a high model order, the estimated variance will be low, but the number of parameters will be high.

This is a tradeoff between residual variance and model order.

Distribution = "Gaussian";   variance Model = "GARCH";   p (model order of GARCH p, q )) = "1";   q (model order of GARCH p, q )) = "1";   r (autoregressive model order of an ARMA r, m ) model) = "1".

Conversely, for a low model order, there are only few parameters but a high variance residual.

We used restricted maximum likelihood to estimate model parameters, with an ante-dependent (first order) variance-covariance matrix structure.

In this case, ∑ i = 1 k S = 1, and the first-order conditional variances of Eq. (14) are necessary in order to decompose the model output variance (Saisana et al., 2005).

Unlike the first-order difference models, the variance and auto-covariance equations here are fully derivable, making the second-order difference models more convenient than the first-order difference models.

Let x s (n) be a real and time-discrete autoregressive model of order 1 (AR-1), variance σ x 2, and sampled at a rate R, which is commonly assumed in the signal processing literature in order to model real sources [20].

In gradient boosting modeling, more accurate estimate of the response variable is obtained through consecutively fitting new models in order to reduce the variance between the predicted and observed responses.