J
Jens Perch Nielsen
Researcher at City University London
Publications - 197
Citations - 4874
Jens Perch Nielsen is an academic researcher from City University London. The author has contributed to research in topics: Estimator & Kernel density estimation. The author has an hindex of 37, co-authored 195 publications receiving 4574 citations. Previous affiliations of Jens Perch Nielsen include Codan & University of Copenhagen.
Papers
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Journal ArticleDOI
A kernel method of estimating structured nonparametric regression based on marginal integration
Oliver Linton,Jens Perch Nielsen +1 more
TL;DR: In this paper, a simple kernel procedure based on marginal integration that estimates the relevant univariate quantity in both additive and multiplicative nonparametric regression is defined, which is used as a preliminary diagnostic tool.
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A simple bias reduction method for density estimation
TL;DR: In this paper, a new method for bias reduction in nonparametric density estimation is proposed, which is a simple, two-stage multiplicative bias correction, and its theoretical properties are investigated, and simulations indicate its practical potential.
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Kernel Density Estimation for Heavy-tailed Distributions using the Champernowne Transformation
TL;DR: In this paper, a unified approach to the estimation of loss distributions is presented, which involves determining the threshold level between large and small losses, and then estimating the density of the transformed data by use of the classical kernel density estimator.
Journal ArticleDOI
Kernel density estimation for heavy-tailed distributions using the champernowne transformation
TL;DR: In this article, a unified approach to the estimation of loss distributions is presented, which involves determining the threshold level between large and small losses, and then estimating the density of the transformed data by use of the classical kernel density estimator.
Journal ArticleDOI
Boundary and Bias Correction in Kernel Hazard Estimation
TL;DR: In this paper, a new class of local linear hazard estimators based on weighted least square kernel estimation is considered, and a new bias correction technique based on bootstrap estimation of additive bias is proposed.