Multiplicative bias correction for discrete kernels
| Autor: | Benedikt Funke, Lynda Harfouche, Nabil Zougab, Smail Adjabi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Physics::General Physics Mathematical optimization 05 social sciences Bandwidth (signal processing) Multiplicative function Estimator Data application 01 natural sciences Cross-validation 010104 statistics & probability Mean integrated squared error 0502 economics and business Probability mass function Applied mathematics Bias correction 0101 mathematics Statistics Probability and Uncertainty 050205 econometrics Mathematics |
| Zdroj: | Statistical Methods & Applications. 27:253-276 |
| ISSN: | 1613-981X 1618-2510 |
| DOI: | 10.1007/s10260-017-0395-x |
| Popis: | In this paper, we prove that two multiplicative bias correction techniques (MBC) can be applied for discrete kernels in the context of probability mass function estimation. First, some properties of the MBC discrete kernel estimators (bias, variance and mean integrated squared error) are investigated. Second, the popular cross-validation technique is adapted for bandwidth selection. Finally, a simulation study and a real data application for discrete data illustrate the performance of the MBC estimators based on dirac discrete uniform and triangular discrete kernels. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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