A fast algorithm for computing least-squares cross-validations for nonparametric conditional kernel density functions
Nonparametric conditional density functions are widely used in applied econometric and statistical modelling because they provide enriched information summaries of the relationships between dependent and independent variables. Although least-squares cross-validation is considered to be the best criterion for bandwidth selection of the kernel estimator of the conditional density, the number of computations required for this procedure grows exponentially as the number of observations increases. A fast algorithm is proposed to reduce this computational cost, and its accuracy and efficiency are verified via numerical experiments. A practical application is also presented to demonstrate the algorithm's potential usefulness.
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- Jeffrey Racine, 2008. "Nonparametric econometrics: a primer (in Russian)," Quantile, Quantile, issue 4, pages 7-56, March.
- Jianqing Fan & Tsz Ho Yim, 2004. "A crossvalidation method for estimating conditional densities," Biometrika, Biometrika Trust, vol. 91(4), pages 819-834, December.
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- Racine, Jeffrey S., 2008. "Nonparametric Econometrics: A Primer," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(1), pages 1-88, March.
- Racine, Jeff, 2002. "Parallel distributed kernel estimation," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 293-302, August.
- Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
- Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
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