On estimating distribution functions using Bernstein polynomials
By Alexandre Leblanc. 2012
113 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Ocean Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/43545c338e8b663af99ccb493a4492d2b5b4a9ec
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Filippo Menczer
- Alessandro Flammini
- Clayton A. Davis
- H. Muller
- Zhenhua Lin
- Onur Varol
- Kai-Cheng Yang
In Ocean Engineering
In Statistics and Probability
- Zhenhua Lin
- Yaqing Chen
- Filippo Menczer
- Alessandro Flammini
- Clayton A. Davis
- H. Muller
- Onur Varol
- Kai-Cheng Yang
Citations in the corpus
- Flexible semiparametric generalized Pareto modeling of the entire range of rainfall amount (2019, unknown): “More recently, Leblanc (2010, 2012a,b) studied the boundary properties of both density and distribution estimators, (see also Petrone, 1999; Ghosal, 2001; Kakizawa, 2004; Bouezmarni and Rolin, 2007, for extensions in a Bayesian and/or multivariate context).”
- Arming the public with artificial intelligence to counter social bots (2019, methodology): “Since the support of our distribution is necessarily the unit interval, the use of KDE in this case leads to biased estimates near the interval boundaries (Leblanc, 2012).”
- Wasserstein Regression (2020, background): “Options to address this include estimating cdfs (e.g., Aggarwal 1955; Read 1972; Falk 1983; Leblanc 2012), or estimating quantile functions (e.”