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Two new nonparametric kernel distribution estimators based on a transformation of the data.
1Laboratoire de Mathématiques et Application, Université de Poitiers, Futuroscope Chasseneuil, France.
Journal of Applied Statistics
|June 16, 2022
Summary
This study introduces novel kernel distribution estimators that demonstrate faster convergence and reduced Mean Integrated Square Error compared to conventional methods. These findings are validated through simulations and real-world data analysis.
Area of Science:
- Statistics
- Machine Learning
Background:
- Kernel distribution estimation is crucial for non-parametric data analysis.
- Existing methods may face limitations in convergence speed and accuracy.
Purpose of the Study:
- To propose and evaluate two novel kernel distribution estimators.
- To compare their performance against conventional estimators.
Main Methods:
- Development of kernel distribution estimators utilizing a data transformation approach.
- Theoretical analysis of estimator properties.
- Comparative study with conventional estimators.
Main Results:
- Proposed estimators exhibit faster convergence rates.
- Proposed estimators achieve a smaller Mean Integrated Square Error.
- Theoretical results confirmed by simulations and real data.
Conclusions:
- The novel data-transformed kernel estimators offer superior performance.
- Appropriate parameter selection is key to enhanced efficiency.
- These estimators provide a valuable advancement in distribution estimation.
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