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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) can… Expand

564 78- PDF

Local asymptotics for polynomial spline regression

- J. Huang
- Mathematics
- 1 October 2003

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 include… Expand

181 46- PDF

Biclustering via sparse singular value decomposition.

- Mihee Lee, H. Shen, J. Huang, J. S. Marron
- Mathematics, Medicine
- Biometrics
- 1 December 2010

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

- J. Huang, N. Liu, M. Pourahmadi, Linxu Liu
- Mathematics
- 1 March 2006

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 the… Expand

364 37

Projection estimation in multiple regression with application to functional ANOVA models

- J. Huang
- Mathematics
- 1 February 1998

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 multivariate… Expand

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 a… Expand

374 34- PDF

Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements

- L. Wang, Hongzhe Li, J. Huang
- Mathematics, Medicine
- Journal of the American Statistical Association
- 1 December 2008

Nonparametric varying-coefficient models are commonly used for analyzing data measured repeatedly over time, including longitudinal and functional response data. Although many procedures have been… Expand

236 27- PDF

Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection

- Lisha Chen, J. Huang
- Mathematics
- 8 October 2012

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 of… Expand

143 26- PDF

A full scale approximation of covariance functions for large spatial data sets

- Huiyan Sang, J. Huang
- Mathematics
- 2012

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 such… Expand

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