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The Statistical Analysis of Compositional Data
TLDR
This paper presents a meta-analyses of compositional data analysis for logratio linear modelling and some of the aspects of such modelling, including the design of matrix covariance structures, as well as problems encountered during and after the studies.
A reliable data-based bandwidth selection method for kernel density estimation
TLDR
The key to the success of the current procedure is the reintroduction of a non- stochastic term which was previously omitted together with use of the bandwidth to reduce bias in estimation without inflating variance.
Robust and efficient estimation by minimising a density power divergence
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
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
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
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
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
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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