Online Variable Kernel Estimator: Application to Microarray Data Analysis

Online Variable Kernel Estimator: Application to Microarray Data Analysis

Yissam Lakhdar, El Hassan Sbai
Copyright: © 2017 |Pages: 35
DOI: 10.4018/IJORIS.2017010104
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Abstract

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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2. Previous Works Of The Online Estimators

Numerous works have studied fairly broadly nonparametric density estimation of Parzen-Rosenblatt type in which the size of the data distribution is predetermined by contrast the work around the online estimate of the probability density function, also called recursive density estimation, are more rare. This online (recursive) estimator allows use of the estimated probability density function from initial data by updating the estimate by taking into account only the temporal arrival of new information.

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