4 citations · 5 across the 6 of their papers we have counts for
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Nonlinear Functional Principal Component Analysis Using Neural Networks
Rou Zhong, Chunming Zhang, Jingxiao Zhang
Functional principal component analysis (FPCA) is an important technique for dimension reduction in functional data analysis (FDA). Classical FPCA method is based on the Karhunen-L…
Kernel partial least squares regression for functional nonlinear models
Rou Zhong, Dongxue Wang, Jingxiao Zhang
Functional regression is very crucial in functional data analysis and a linear relationship between scalar response and functional predictor is often assumed. However, the linear a…
Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data
Rou Zhong, Chunming Zhang, Jingxiao Zhang
In longitudinal study, it is common that response and covariate are not measured at the same time, which complicates the analysis to a large extent. In this paper, we take into acc…
Sparse logistic functional principal component analysis for binary data
Rou Zhong, Shishi Liu, Haocheng Li +1
Functional binary datasets occur frequently in real practice, whereas discrete characteristics of the data can bring challenges to model estimation. In this paper, we propose a spa…
Functional principal component analysis estimator for non-Gaussian data
Rou Zhong, Shishi Liu, Haocheng Li +1
Functional principal component analysis (FPCA) could become invalid when data involve non-Gaussian features. Therefore, we aim to develop a general FPCA method to adapt to such non…
Robust Functional Principal Component Analysis for Non-Gaussian Longitudinal Data
Rou Zhong, Shishi Liu, Jingxiao Zhang +1
Functional principal component analysis is essential in functional data analysis, but the inferences will become unconvincing when some non-Gaussian characteristics occur, such as…