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Also note that in the D-optimal design, the input giving the maximum prediction variance of its output is selected under some conditions, which is an important property.
Furthermore, the prediction capability of a design can be assessed using G-optimality criterion, which searches for the design that minimizes the maximum prediction variance over the experimental region.
A design criterion related to G-optimality is G-efficiency, defined as {text{G-efficiency}} = 100 times left( {frac{p/n}{text{MPV}}} right), (4 where (p/n) is the average prediction variance (APV) and MPV is the maximum prediction variance.
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The MSEP incorporates both the prediction variance and the prediction bias, which results from using maximum likelihood estimates of the parameters of the fitted linear predictor.
This algorithm chooses runs that minimize the integral of the prediction variance across the factor space.
The proposed criterion is derived by performing an asymptotic expansion to the conditional prediction variance.
Lack of fit results in increased prediction squared bias, and overfitting results in increased prediction variance.
In addition to thinning, aggregating across predictions from multiple models also decreases the prediction variance.
Validation of the prediction variances was performed using the standardized squared prediction errors (SSEs).
Modeling should thus focus on the physiological limits of species for maximum predictions.
The ordinary kriging method was used to develop predictions and krige prediction variances for each respondent.
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