Bagging cross-validated bandwidths with application to big data
| Autor: | Daniel Barreiro-Ures, Mario Francisco-Fernández, Cao R, Jeffrey D. Hart |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
business.industry Applied Mathematics General Mathematics 05 social sciences Big data computer.software_genre 01 natural sciences Agricultural and Biological Sciences (miscellaneous) 010104 statistics & probability 0502 economics and business Data mining 0101 mathematics Statistics Probability and Uncertainty General Agricultural and Biological Sciences business computer 050205 econometrics Mathematics |
| Zdroj: | Biometrika. 108:981-988 |
| ISSN: | 1464-3510 0006-3444 |
| Popis: | Summary Hall & Robinson (2009) proposed and analysed the use of bagged cross-validation to choose the bandwidth of a kernel density estimator. They established that bagging greatly reduces the noise inherent in ordinary cross-validation, and hence leads to a more efficient bandwidth selector. The asymptotic theory of Hall & Robinson (2009) assumes that $N$, the number of bagged subsamples, is $\infty$. We expand upon their theoretical results by allowing $N$ to be finite, as it is in practice. Our results indicate an important difference in the rate of convergence of the bagged cross-validation bandwidth for the cases $N=\infty$ and $N |
| Databáze: | OpenAIRE |
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