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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Uniform Bahadur Representation for LocalPolynomial Estimates of M-Regressionand Its Application to The Additive Model
TL;DR: In this article, the authors used local polynomial fitting to estimate the nonparametric M-regression function for strongly mixing stationary processes and established a strong uniform consistency rate for the Bahadur representation of estimators of the regression function.
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Identification and Nonparametric Estimation of a Transformed Additively Separable Model
TL;DR: In this paper, an estimation algorithm is proposed for each of the model's unknown components when r(x, z) represents a conditional mean function, and the resulting estimators use marginal integration and have a limiting normal distribution with a faster rate of convergence than unrestricted nonparametric alternatives.
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An Improved Bootstrap Test of Stochastic Dominance
TL;DR: In this paper, the authors proposed a new method of testing stochastic dominance that improves on existing tests based on the standard bootstrap or subsampling, which admits infinite as well as finite dimensional unknown parameters, so that the variables are allowed to be residuals from nonparametric and semiparametric models.
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Estimation of the Kronecker Covariance Model by Quadratic Form
Oliver Linton,Haihan Tang +1 more
TL;DR: In this article, the quadratic form estimator of the Kronecker product model for covariance matrices is proposed and shown to be consistent in a relative Frobenius norm sense.
ReportDOI
A simple and efficient estimation method for models with nonignorable missing data
TL;DR: In this article, the authors proposed an estimation method based on the Generalized Method of Moments (hereafter GMM), which is consistent and asymptotically normal regardless of the number of moments chosen.