Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernal density estimation

By Tarn Duong, Martin L. Hazelton. 2005

66 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cc98efdf5fcbbe5e555067800f9357152bc98d15

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. Jose Emmanuel Chacón
2. Tarn Duong
3. Arthur Charpentier
4. Gery Geenens
5. Davy Paindaveine
6. Martin L. Hazelton
7. M. Stewart
8. Madeleine Cule

In Statistical and Nonlinear Physics

1. Arthur Charpentier
2. Gery Geenens
3. Davy Paindaveine
4. Tarn Duong
5. Hamed Haddadi
6. Richard J. Samworth
7. Yen-Chi Chen
8. Krikamol Muandet

In Statistics and Probability

1. Richard J. Samworth
2. Jose Emmanuel Chacón
3. Tarn Duong
4. Gery Geenens
5. Arthur Charpentier
6. Davy Paindaveine
7. M. Stewart
8. Madeleine Cule

Citations in the corpus

• Cross‐validation Bandwidth Matrices for Multivariate Kernel Density Estimation (2005, background): “The following result, proved by Duong and Hazelton (2004), is therefore useful.”
• ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R (2007, background): “More extensive simulation studies are reported in Duong and Hazelton (2003, 2005b).”
• Deep Private-Feature Extraction (2018, methodology): “By employing the kernel density estimation, we can efficiently estimate pðfÞ [43].”
• Multivariate plug-in bandwidth selection with unconstrained pilot bandwidth matrices (2010, background): “As in Duong and Hazelton (2005a), this rate may be defined to be n−α if vec(ĤPI − HAMISE) = OP (Jdn−α)vec HAMISE, where Jd denotes the d × d matrix of ones, and the OP notation is meant to be applied element-wise.”
• Maximum likelihood estimation of a multi‐dimensional log‐concave density (2008, background): “Despite recent progress (e.g. Duong and Hazelton (2003, 2005), Zhang et al. (2006), Chacón et al. (2010) and Chacón (2009)) significant practical challenges remain.”
• Probit Transformation for Nonparametric Kernel Estimation of the Copula Density (2014, methodology): “If fST is stretched along a particular direction of R2, which will be the case if c itself is so on the unit square, it is greatly beneficial to the estimator that smoothing be applied in that direction (Duong and Hazelton, 2005), and so h12 should be selected accordingly.”