In order to estimate the out-of sample performance of the tumor probability predictions, we used what we termed "leave-cell-out" and "leave-mouse-out" cross-validation for cell and brain tissue data, respectively.
Out-of-sample performance of discrete-time short-term interest models, with H. Li and F. Zhao, Journal of Business and Economic Statistics 22 (2004), 457-473.
In addition, the out-of-sample performance of the semi-parametric model is superior to that of the linear model.
However, our empirical results further indicate that the out-of-sample performance of the MVSWARCH-based hedge ratio is statistically marginal when investors hold a well-diversified market portfolio as their spot position and tranquil periods are experienced.
The model, estimated using a large set of macroeconomic variables, is able to predict 76% of entries into crisis while sending 36% of false alarms and has rather good out-of-sample performance.
Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio.
To further investigate the models' out-of-sample performance, we apply a rolling window forecast similar to the method used in [10, 49].
However, the spline-based model seems to provide a closer fit to the data, which may lead to overfitting and poor out-of-sample performance.
The validation of the model's existing value and future accident prediction capabilities is done using the in-fit-sample and out-of-sample performance evaluation techniques, respectively.
In Table 7, we present statistics on the 'out-of-sample' performance of the Google model compared to the base model.
