Weighted samples, kernel density estimators and convergence

By F. J. G. Gisbert. 2003

75 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Political Science and International Relations

View Paper: https://www.semanticscholar.org/paper/5f3f44c1ee96e871f71b6d412405585c4ee486d5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Ying Liu
2. K. Gluschenko

In Political Science and International Relations

1. K. Gluschenko

Citations in the corpus

• Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications (2018, methodology): “This procedure is very similar to weighted kernel density estimation [11, 14].”
• Measuring regional inequality: to weight or not to weight? (2018, methodology): “Then what is difficult to interpret, the unweighted coefficient of variation or the weighted one? Gisbert (2003) gives the reason for the weighting by population in a kernel density of crosscountry income distribution, based on reasoning essentially similar to that in Lessmann (2014), albeit in the ”