Robust location and spread measures for nonparametric probability density function estimation
Related Experiment Video
Updated: Jun 19, 2026

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
Published on: September 16, 2020
1Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35. 29071 Málaga, Spain. ezeqlr@lcc.uma.es
Abstract:
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.
Related Concept Videos
Probability Distributions
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...
Distributions to Estimate Population Parameter
Probability Histograms
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Applications of Integration to Probability Density Functions
