Bias in nearest-neighbor hazard estimation
In nonparametric curve estimation, the smoothing parameter is critical for performance. In order to estimate the hazard rate, we compare nearest neighbor selectors that minimize the quadratic, the Kullback-Leibler, and the uniform loss. These measures result in a rule of thumb, a cross-validation, and a plug-in selector. A Monte Carlo simulation within the three-parameter exponentiated Weibull distribution indicates that a counter-factual normal distribution, as an input to the selector, does provide a good rule of thumb. If bias is the main concern, minimizing the uniform loss yields the best results, but at the cost of very high variability. Cross-validation has a similar bias to the rule of thumb, but also with high variability.
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- Kiefer, Nicholas M. & Larson, C. Erik, 2007.
"A simulation estimator for testing the time homogeneity of credit rating transitions,"
Journal of Empirical Finance,
Elsevier, vol. 14(5), pages 818-835, December.
- Kiefer, Nicholas M. & Larson, C. Erik, 2006. "A Simulation Estimator for Testing the Time Homogeneity of Credit Rating Transition," Working Papers 06-10, Cornell University, Center for Analytic Economics.
- Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
- Ralescu, S. S., 1995. "The Law of the Iterated Logarithm for the Multivariate Nearest Neighbor Density Estimators," Journal of Multivariate Analysis, Elsevier, vol. 53(1), pages 159-179, April.
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