Kernel estimation for a superpopulation probability density function under informative selection

METRON ◽  
2017 ◽  
Vol 75 (3) ◽  
pp. 301-318 ◽  
Author(s):  
Daniel Bonnéry ◽  
F. Jay Breidt ◽  
François Coquet
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Kernel Estimation

Online Variable Kernel Estimator

2017 ◽  
Vol 8 (1) ◽  
pp. 58-92
Author(s):  
Yissam Lakhdar ◽  
El Hassan Sbai
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Maximum Entropy Principle ◽  
Kernel Estimation ◽  
Simulated Data ◽  
Kernel Estimator ◽  
Variable Kernel ◽  
Entropy Principle ◽  
And Performance

In this work, the authors propose a novel method called online variable kernel estimation of the probability density function (pdf). This new online estimator combines the characteristics and properties of two estimators namely nearest neighbors estimator and the Parzen-Rosenblatt estimator. Their approach allows a compact online adaptation of the estimated probability density function from the new arrival data. The performance of the online variable kernel estimator (OVKE) depends on the choice of the bandwidth. The authors present in this article a new technique for determining the optimal smoothing parameter of OVKE based on the maximum entropy principle (MEP). The robustness and performance of the proposed approach are demonstrated by examples of online estimation of real and simulated data distributions.

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