## Abstract In this paper, a general kernel density estimator has been introduced and discussed for multivariate processes in order to provide enhanced realβtime performance monitoring. The proposed approach is based upon the concept of kernel density function, which is more appropriate to the unde
Classification procedures using multivariate variable kernel density estimate
β Scribed by Adam Krzyzak
- Publisher
- Elsevier Science
- Year
- 1983
- Tongue
- English
- Weight
- 334 KB
- Volume
- 1
- Category
- Article
- ISSN
- 0167-8655
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π SIMILAR VOLUMES
Multivariate kernel density estimators are known to systematically deviate from the true value near critical points of the density surface. To overcome this difficulty a method based on Rao Blackwell's theorem is proposed. Local corrections of kernel density estimators are achieved by conditioning t
The problem of nonparametric estimation of a multivariate density function is addressed. In particular, a general class of estimators with favorable asymptotic performance (bias, variance, rate of convergence) is proposed. The proposed estimators are characterized by the flatness near the origin of