Robust location and spread measures for nonparametric probability density function estimation.
Robustness against outliers is a desirable property of any unsupervised learning scheme. In particular, probability density estimators benefit from incorporating this feature. A possible strategy to achieve this goal is to substitute the sample mean and the sample covariance matrix by more robust location and spread estimators. Here we use the L1-median to develop a nonparametric probability density function (PDF) estimator. We prove its most relevant properties, and we show its performance in density estimation and classification applications.
López-Rubio
Ezequiel
E
Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35. 29071 Málaga, Spain. ezeqlr@lcc.uma.es
D000465:Algorithms; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011336:Probability; D013223:Statistics as Topic / Q000379:methods*; D018709:Statistics, Nonparametric