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The Statistical Analysis of Compositional Data
- M. C. Jones, J. Aitchison
- Mathematics
- 21 August 1986
TLDR
A reliable data-based bandwidth selection method for kernel density estimation
- S. Sheather, M. C. Jones
- Computer Science
- 1 July 1991
TLDR
Robust and efficient estimation by minimising a density power divergence
- A. Basu, Ian R. Harris, N. Hjort, M. C. Jones
- Mathematics
- 1 September 1998
A minimum divergence estimation method is developed for robust parameter estimation. The proposed approach uses new density-based divergences which, unlike existing methods of this type such as…
Local Linear Quantile Regression
- Keming Yu, M. C. Jones
- Mathematics
- 1 March 1998
Abstract In this article we study nonparametric regression quantile estimation by kernel weighted local linear fitting. Two such estimators are considered. One is based on localizing the…
Families of distributions arising from distributions of order statistics
- M. C. Jones
- Mathematics
- 1 June 2004
Consider starting from a symmetric distributionF on ℜ and generating a family of distributions from it by employing two parameters whose role is to introduce skewness and to vary tail weight. The…
A Brief Survey of Bandwidth Selection for Density Estimation
- M. C. Jones, J. Marron, S. Sheather
- Engineering
- 1 March 1996
Abstract There has been major progress in recent years in data-based bandwidth selection for kernel density estimation. Some “second generation” methods, including plug-in and smoothed bootstrap…
Locally parametric nonparametric density estimation
- N. Hjort, M. C. Jones
- Mathematics
- 1 August 1996
This paper develops a nonparametric density estimator with parametric overtones. Suppose f(x, θ) is some family of densities, indexed by a vector of parameters θ. We define a local kernel-smoothed…
Simple boundary correction for kernel density estimation
- M. C. Jones
- Mathematics
- 1 September 1993
If a probability density function has bounded support, kernel density estimates often overspill the boundaries and are consequently especially biased at and near these edges. In this paper, we…
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