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Explanation:
The Cramér-von Mises test provides a comprehensive evaluation of goodness-of-fit, offering balanced sensitivity across both central and tail deviations, making it ideal for large datasets where assessing overall model robustness across all areas is essential. Its ability to detect subtle deviations throughout the distribution ensures that models are well-calibrated for diverse scenarios. This makes it particularly valuable for applications requiring consistent performance across both normal and extreme conditions.
A is incorrect. While it balances sensitivity, it does not focus exclusively on tails.
B is incorrect. The CvM test is less suited for smaller datasets due to computational intensity.
C is incorrect. Bias correction is secondary to balanced evaluation across distribution.
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Q.6500 In evaluating PIT distributions from VaR models, the Cramér-von Mises (CvM) test is often preferred for large datasets. What is the primary reason for its use, and how does it aid in assessing model robustness?
A
It maximizes sensitivity in both model tails and central distributions.
B
It efficiently processes smaller datasets with precision.
C
It simplifies correction of model biases across multiple comparisons.
D
It globally evaluates goodness-of-fit, balancing central and tail deviations.