• Publications
  • Influence
Sparse principal component analysis via regularized low rank matrix approximation
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction in applications throughout science and engineering. However, the principal components (PCs) canExpand
  • 564
  • 78
  • PDF
Local asymptotics for polynomial spline regression
In this paper we develop a general theory of local asymptotics for least squares estimates over polynomial spline spaces in a regression problem. The polynomial spline spaces we consider includeExpand
  • 181
  • 46
  • PDF
Biclustering via sparse singular value decomposition.
Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices.Expand
  • 210
  • 43
  • PDF
Covariance matrix selection and estimation via penalised normal likelihood
We propose a nonparametric method for identifying parsimony and for producing a statistically efficient estimator of a large covariance matrix. We reparameterise a covariance matrix through theExpand
  • 364
  • 37
Projection estimation in multiple regression with application to functional ANOVA models
A general theory on rates of convergence of the least-squares projection estimate in multiple regression is developed. The theory is applied to the functional ANOVA model, where the multivariateExpand
  • 152
  • 35
  • PDF
Varying‐coefficient models and basis function approximations for the analysis of repeated measurements
A global smoothing procedure is developed using basis function approximations for estimating the parameters of a varying-coefficient model with repeated measurements. Inference procedures based on aExpand
  • 374
  • 34
  • PDF
Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements
Nonparametric varying-coefficient models are commonly used for analyzing data measured repeatedly over time, including longitudinal and functional response data. Although many procedures have beenExpand
  • 236
  • 27
  • PDF
Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection
The reduced-rank regression is an effective method in predicting multiple response variables from the same set of predictor variables. It reduces the number of model parameters and takes advantage ofExpand
  • 143
  • 26
  • PDF
A full scale approximation of covariance functions for large spatial data sets
Summary.  Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of suchExpand
  • 195
  • 24
  • PDF
Polynomial Spline Estimation and Inference for Varying Coefficient Models with Longitudinal Data
We consider nonparametric estimation of coefficient functions in a varying coefficient model of the form Yij = X T i (tij)β(tij)+ i(tij) based on longitudinal observations {(Yij , Xi(tij), tij), i =Expand
  • 169
  • 21
  • PDF