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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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A Semiparametric Panel Model for Unbalanced Data with Application to Climate Change in the United Kingdom

TL;DR: In this paper, a semi-parametric panel model was developed to explain the trend in UK temperatures and other weather outcomes over the last century, which allowed the trend to evolve in a nonparametric way so that a fuller picture of the evolution of common temperature in the medium timescale.
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The October 2016 Sterling Flash Episode: When Liquidity Disappeared from One of the World's Most Liquid Markets

TL;DR: In this paper, the authors provide an in-depth analysis of the evolution of liquidity during the flash episode in sterling during the early hours of 7 October 2016. And they examine a number of estimates both of the cost of trading, and the price impact of executed transactions.
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Estimating semiparametric ARCH (∞) models by kernel smoothing methods

TL;DR: In this paper, the authors investigate a class of semiparametric ARCH models that includes the partially nonparametric (PNP) model introduced by Engle and Ng (1993) and which allows for both flexible dynamics and flexible function form with regard to the 'news impact' function.

Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance

TL;DR: In this paper , the authors investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance, and propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments.
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Estimating additive nonparametric models by partial Lq norm: the curse of fractionality

TL;DR: In this paper, a new method for estimating additive nonparametric regression models based on taking the Lq median of a sample of kernel estimators is proposed, and the consistency and asymptotic normality of their procedures are established.