Lijian Yang

Prof. Dr. Lijian Yang

Department of Industrial Engineering, Tsinghua University, Beijing, China
orcid

Research Focus & Metrics

Academic Career Length

29Years (1998-Present)

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

Representative Publications

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24 January 2012 in Journal of Nonparametric Statistics
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Journal Article
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GCGuanqun Cao
Guanqun Cao
Lijian YangLijian Yang
Lijian Yang
DTDavid Todem
David Todem
A polynomial spline estimator is proposed for the mean function of dense functional data together with a simultaneous confidence band which is asymptotically correct. In addition, the spline estimator and its accompanying confidence band enjoy oracle efficiency in the sense that they are asymptotically the same as if all random trajectories are observed entirely and without errors. The confidence band is also extended to the difference of mean functions of two populations of functional data. Simulation experiments provide strong evidence that corroborates the asymptotic theory while computing is efficient. The confidence band procedure is illustrated by analysing the near-infrared spectroscopy data.

1 April 2002 in Econometric Theory
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Journal Article
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SSStefan Sperlich
Stefan Sperlich
DTDag Tjøstheim
Dag Tjøstheim
Lijian YangLijian Yang
Lijian Yang
We consider an additive model with second-order interaction terms. Both marginal integration estimators and a combined backfitting-integration estimator are proposed for all components of the model and their derivatives. The corresponding asymptotic distributions are derived. Moreover, two test statistics for testing the presence of interactions are proposed. Asymptotics for the test functions and local power results are obtained. Because direct implementation of the test procedure based on the asymptotics would produce inaccurate results unless the number of observations is very large, a bootstrap procedure is provided, which is applicable for small or moderate sample sizes. Further, based on these methods a general test for additivity is developed. Estimation and testing methods are shown to work well in simulation studies. Finally, our methods are illustrated on a five-dimensional production function for a set of Wisconsin farm data. In particular, the separability hypothesis for the production function is discussed.

1 December 2007 in The Annals of Statistics
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Journal Article
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LWLi Wang
Li Wang
Lijian YangLijian Yang
Lijian Yang
Application of nonparametric and semiparametric regression techniques to high-dimensional time series data has been hampered due to the lack of effective tools to address the “curse of dimensionality.” Under rather weak conditions, we propose spline-backfitted kernel estimators of the component functions for the nonlinear additive time series data that are both computationally expedient so they are usable for analyzing very high-dimensional time series, and theoretically reliable so inference can be made on the component functions with confidence. Simulation experiments have provided strong evidence that corroborates the asymptotic theory.

14 April 2004 in Journal of the Royal Statistical Society Series B: Statistical Methodology
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JHJianhua Z. Huang
Jianhua Z. Huang
Lijian YangLijian Yang
Lijian Yang

1 September 1999 in Journal of Time Series Analysis
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Journal Article
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Lijian YangLijian Yang
Lijian Yang
WHWolfgang Karl Härdle
Wolfgang Karl Härdle
JNJens Perch Nielsen
Jens Perch Nielsen

1 July 2000 in Journal of Time Series Analysis
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Journal Article
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RTRolf Tschernig
Rolf Tschernig
Lijian YangLijian Yang
Lijian Yang