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However, due to the strong autocorrelation in the forward premium, which shows estimated autoregressive roots close and in some cases statistically indistinguishable from one, recent literature has cast doubt on the finite sample accuracy of these tests.
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In practice this is impossible due to finite sampling rates.
This is relevant for real situations where deviations from the average relationship exist due to linkage and finite sampling [ 14].
Finite sample local accuracy bounds are not available for other machine learning algorithms.
The analogous finite sample local accuracy bounds are not available for other methods (with the exception of linear regression [ 10]).
We first consider the finite sample case, for which expected ranking accuracy can be estimated by means of the area under the ROC curve (AUC), and subsequently, we further generalize these results to the underlying distributions.
This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bound on an Artificial Neural Network's (ANN) accuracy statistic from a finite sample set.
In a series of Monte Carlo experiments, the finite sample properties of three variants of the predictive accuracy test are examined.
Numerical results also indicate this new interval has better average coverage accuracy than the best existing intervals in finite sample sizes.
Moreover, because the finite sample bias of 2SLS is not monotonically decreasing in the degree of measurement accuracy, the most accurate imputation method is not necessarily the method that minimizes the bias of 2SLS.
In this study we have only applied finite sample local minimax bounds for Tikhonov kernel learning (i.e. using squared error loss) and obtained improved accuracy.
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