19 citations · 34 across the 12 of their papers we have counts for
12 papers
From sparse to dense functional data in high dimensions: Revisiting phase transitions from a non-asymptotic perspective
Shaojun Guo, Dong Li, Xinghao Qiao +1
Nonparametric estimation of the mean and covariance functions is ubiquitous in functional data analysis and local linear smoothing techniques are most frequently used. Zhang and Wa…
Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization
Yirui Liu, Xinghao Qiao, Yulong Pei +1
This paper introduces the Deep Functional Factor Model (DF2M), a Bayesian nonparametric model designed for analysis of high-dimensional functional time series. DF2M is built upon t…
EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model
Yirui Liu, Xinghao Qiao, Liying Wang +1
Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focu…
Adaptive Functional Thresholding for Sparse Covariance Function Estimation in High Dimensions
Qin Fang, Shaojun Guo, Xinghao Qiao
Covariance function estimation is a fundamental task in multivariate functional data analysis and arises in many applications. In this paper, we consider estimating sparse covarian…
Factor modelling for high-dimensional functional time series
Shaojun Guo, Xinghao Qiao, Qingsong Wang +1
Many economic and scientific problems involve the analysis of high-dimensional functional time series, where the number of functional variables diverges as the number of serial…
An autocovariance-based learning framework for high-dimensional functional time series
Jinyuan Chang, Cheng Chen, Xinghao Qiao +1
Many scientific and economic applications involve the statistical learning of high-dimensional functional time series, where the number of functional variables is comparable to, or…
Finite Sample Theory for High-Dimensional Functional/Scalar Time Series with Applications
Qin Fang, Shaojun Guo, Xinghao Qiao
Statistical analysis of high-dimensional functional times series arises in various applications. Under this scenario, in addition to the intrinsic infinite-dimensionality of functi…
On Consistency and Sparsity for High-Dimensional Functional Time Series with Application to Autoregressions
Shaojun Guo, Xinghao Qiao
Modelling a large collection of functional time series arises in a broad spectral of real applications. Under such a scenario, not only the number of functional variables can be di…
CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models
Yirui Liu, Xinghao Qiao, Jessica Lam
Current variational inference methods for hierarchical Bayesian nonparametric models can neither characterize the correlation structure among latent variables due to the mean-field…
A General Theory for Large-Scale Curve Time Series via Functional Stability Measure
Shaojun Guo, Xinghao Qiao
Modelling a large bundle of curves arises in a broad spectrum of real applications. However, existing literature relies primarily on the critical assumption of independent curve ob…
Functional Linear Regression: Dependence and Error Contamination
Cheng Chen, Shaojun Guo, Xinghao Qiao
Functional linear regression is an important topic in functional data analysis. It is commonly assumed that samples of the functional predictor are independent realizations of an u…
Homogeneity Pursuit in Single Index Models based Panel Data Analysis
Heng Lian, Xinghao Qiao, Wenyang Zhang
Panel data analysis is an important topic in statistics and econometrics. Traditionally, in panel data analysis, all individuals are assumed to share the same unknown parameters, e…