most citedRobust Functional Principal Component Analysis for Non-Gaussian Longitudinal Data

4 citations · 5 across the 5 of their papers we have counts for

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stat.ME2021

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…

stat.ME2021

Model Averaging Estimation for Partially Linear Functional Score Models

Shishi Liu, Hao Zhang, Jingxiao Zhang

This paper is concerned with model averaging estimation for partially linear functional score models. These models predict a scalar response using both parametric effect of scalar…

stat.ME2021

Model Averaging by Cross-validation for Partially Linear Functional Additive Models

Shishi Liu, Jingxiao Zhang

In this paper, we propose a model averaging approach for addressing model uncertainty in the context of partial linear functional additive models. These models are designed to desc…

stat.ME2021★ 1 cited

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…

stat.ME2021★ 4 cited

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…