O
Oliver Linton
Researcher at University of Cambridge
Publications - 447
Citations - 13008
Oliver Linton is an academic researcher from University of Cambridge. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 55, co-authored 425 publications receiving 12055 citations. Previous affiliations of Oliver Linton include University of Illinois at Urbana–Champaign & Yale University.
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Optimal Smoothing for a Computationallyand StatisticallyEfficient Single Index Estimator
TL;DR: Based on local linear kernel smoother, this paper proposed an estimation method to estimate the single-index model without under-smoothing, which is asymptotically normal and most efficient in thesemi-parametric sense.
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Global Bahadur Representation For NonParametric Censored Regression Quantiles and its Applications
TL;DR: This article derived a global Bahadur representation for a class of locally weighted polynomial estimators, which is sufficiently accurate for many further theoretical analyses including inference, using results on the strong uniform convergence rate of U-processes.
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Estimation of a Multiplicative Covariance Structure in the Large Dimensional Case
TL;DR: In this paper, the authors proposed a Kronecker product model for covariance matrices in the large dimensional case and established rates of convergence and central limit theorems (CLT) for their estimators.
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Global Bahadur Representation for Nonparametric Censored Regression Quantiles and its Applications
TL;DR: This paper derived a global Bahadur representation for the weighted local polynomial estimators, which is sufficiently accurate for many further theoretical analyses including inference, and considered two applications in detail: estimation of the average derivative and the component functions in additive quantile regression models.
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Second-order approximation for adaptive regression estimators
Oliver Linton,Zhijie Xiao +1 more
TL;DR: In this paper, the authors derived the asymptotic distribution of the second-order effect of an adaptive estimator in a linear regression whose error density is of unknown functional form and showed how the choice of smoothing parameters influences the estimator through higher order terms.