ASYMPTOTICS FOR GENERAL MULTIVARIATE KERNEL DENSITY DERIVATIVE ESTIMATORS

By Jose Emmanuel Chacón, Tarn Duong, Matt P. Wand. 2011

145 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cd4e2e3ed511b84995e2ac7b2f90ecaee0fe6074

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

1. Larry A. Wasserman
2. Robert Tibshirani
3. Christopher R. Genovese
4. Yen-Chi Chen
5. Alexandre Leblanc
6. Mehmet Caner
7. Anders Bredahl Kock

In Statistics and Probability

1. Larry A. Wasserman
2. Robert Tibshirani
3. Yen-Chi Chen
4. M. Di Marzio
5. A. Panzera
6. Charles C. Taylor
7. Christopher R. Genovese
8. Bradley Efron

Citations in the corpus

• Kernel density estimation on the torus (2011, background): “Markedly, see the very detailed discussion on the importance of multivariate kernel density derivative estimation made by Chacón et al. (2010). In Section 2, as the starting point, we define a class of suitable kernels whose order is defined in close analogy to the linear case.”
• Nonparametric modal regression (2014, methodology): “Then use the same arguments in Chacón et al. (2011); Chacón and Duong (2013) to show that integrating bias and variance over x still yield the same rate of convergence.”
• A tutorial on kernel density estimation and recent advances (2017, background): “Similarly to Bh(x) and En x ð Þ in the density estimation, there are explicit formulas for the bias and stochastic variation of density derivative estimation; see [19] for more details.”