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Weight-calibrated estimation for factor models of high-dimensional time series
Xinghao Qiao, Zihan Wang, Qiwei Yao +1
The factor modeling for high-dimensional time series is powerful in discovering latent common components for dimension reduction and information extraction. Most available estimati…
On a new robust method of inference for general time series models
Zihan Wang, Xinghao Qiao, Dong Li +1
In this article, we propose a novel logistic quasi-maximum likelihood estimation (LQMLE) for general parametric time series models. Compared to the classical Gaussian QMLE and exis…
Large covariance matrix estimation with factor-assisted variable clustering
Dong Li, Xinghao Qiao, Cheng Yu
This paper studies the covariance matrix estimation for high-dimensional time series within a new framework that combines low-rank factor and latent variable-specific cluster struc…
Functional knockoffs selection with applications to functional data analysis in high dimensions
Xinghao Qiao, Mingya Long, Qizhai Li
The knockoffs is a recently proposed powerful framework that effectively controls the false discovery rate (FDR) for variable selection. However, none of the existing knockoff solu…
Factor-guided estimation of large covariance matrix function with conditional functional sparsity
Dong Li, Xinghao Qiao, Zihan Wang
This paper addresses the fundamental task of estimating covariance matrix functions for high-dimensional functional data/functional time series. We consider two functional factor s…