Zudi Lu

Prof. Dr. Zudi Lu

Mathematical Sciences and Southampton Statistical Sciences Research Institute, University of Southampton, Southampton SO17 1BJ, UK
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Research Focus & Metrics

Academic Career Length

31Years (1996-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 October 2003 in Journal of the Royal Statistical Society: Series D (The Statistician)
Verified
Journal Article
Closed access
PDF file
KYKeming Yu
Keming Yu
ZLZudi Lu
Zudi Lu
JSJulian Stander
Julian Stander

1 December 2004 in The Annals of Statistics
Verified
Journal Article
Open access
MHMarc Hallin
Marc Hallin
ZLZudi Lu
Zudi Lu
LTLanh T. Tran
Lanh T. Tran
A local linear kernel estimator of the regression function x↦g(x):=E[Yi|Xi=x], x∈ℝd, of a stationary (d+1)-dimensional spatial process {(Y i,Xi),i∈ℤN} observed over a rectangular domain of the form ℐn:={i=(i1,…,iN)∈ℤN|1≤ik≤nk,k=1,…,N}, n=(n1,…,nN)∈ℤN, is proposed and investigated. Under mild regularity assumptions, asymptotic normality of the estimators of g(x) and its derivatives is established. Appropriate choices of the bandwidths are proposed. The spatial process is assumed to satisfy some very general mixing conditions, generalizing classical time-series strong mixing concepts. The size of the rectangular domain ℐn is allowed to tend to infinity at different rates depending on the direction in ℤN.

1 August 2009 in Bernoulli
Verified
Journal Article
Open access
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MHMarc Hallin
Marc Hallin
ZLZudi Lu
Zudi Lu
KYKeming Yu
Keming Yu
Project Euclid - mathematics and statistics online

1 June 2006 in The Annals of Statistics
Verified
Journal Article
Open access
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JGJiti Gao
Jiti Gao
ZLZudi Lu
Zudi Lu
DTDag Tjøstheim
Dag Tjøstheim
Nonparametric methods have been very popular in the last couple of decades in time series and regression, but no such development has taken place for spatial models. A rather obvious reason for this is the curse of dimensionality. For spatial data on a grid evaluating the conditional mean given its closest neighbors requires a four-dimensional nonparametric regression. In this paper a semiparametric spatial regression approach is proposed to avoid this problem. An estimation procedure based on combining the so-called marginal integration technique with local linear kernel estimation is developed in the semiparametric spatial regression setting. Asymptotic distributions are established under some mild conditions. The same convergence rates as in the one-dimensional regression case are established. An application of the methodology to the classical Mercer and Hall wheat data set is given and indicates that one directional component appears to be nonlinear, which has gone unnoticed in earlier analyses.

1 December 2009 in The Annals of Statistics
Verified
Journal Article
Open access
PDF file
JGJiti Gao
Jiti Gao
MKMaxwell King
Maxwell King
ZLZudi Lu
Zudi Lu
DTDag Tjøstheim
Dag Tjøstheim
This paper considers a class of nonparametric autoregressive models with nonstationarity. We propose a nonparametric kernel test for the conditional mean and then establish an asymptotic distribution of the proposed test. Both the setting and the results differ from earlier work on nonparametric autoregression with stationarity. In addition, we develop a new bootstrap simulation scheme for the selection of a suitable bandwidth parameter involved in the kernel test as well as the choice of a simulated critical value. The finite-sample performance of the proposed test is assessed using one simulated example and one real data example.

1 January 2004 in Journal of Multivariate Analysis
Verified
Journal Article
Closed access
MHMarc Hallin
Marc Hallin
ZLZudi Lu
Zudi Lu
LTLanh T. Tran
Lanh T. Tran