The stochastic approximation method for the estimation of a multivariate probability density
By Abdelkader Mokkadem, Mariane Pelletier, Yousri Slaoui. 2008
82 citations recorded by the source corpus.
Abstract We apply the stochastic approximation method to construct a large class of recursive kernel estimators of a probability density, including the one introduced by Hall and Patil [1994. On the efficiency of on-line density estimators. IEEE Trans. Inform. Theory 40, 1504–1512]. We study the properties of these estimators and compare them with Rosenblatt's nonrecursive estimator. It turns out that, for pointwise estimation, it is preferable to use the nonrecursive Rosenblatt's kernel estimator rather than any recursive estimator. A contrario, for estimation by confidence intervals, it is better to use a recursive estimator rather than Rosenblatt's estimator.
Subfields: Discrete Mathematics and Combinatorics, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/4bc117bd3bfe7db796a168fa18e6cce8d6f35a69
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In Discrete Mathematics and Combinatorics
In Management Science and Operations Research
In Statistics and Probability
Citations in the corpus
- Bandwidth Selection for Recursive Kernel Density Estimators Defined by Stochastic Approximation Method (2014, methodology): “One says that Condition (6) was introduced by Galambos and Seneta [16] to define regularly varying sequences (see also Bojanic and Seneta [17]) and by Mokkadem and Pelletier [18] in the context of stochastic approximation algorithms.”