421 citations · 495 across the 8 of their papers we have counts for
8 papers
Factor Strength Estimation in Vector and Matrix Time Series Factor Models
Weilin Chen, Clifford Lam
Most factor modelling research in vector or matrix-valued time series assume all factors are pervasive/strong and leave weaker factors and their corresponding series to the noise.…
Rank and Factor Loadings Estimation in Time Series Tensor Factor Model by Pre-averaging
Weilin Chen, Clifford Lam
Tensor time series data appears naturally in a lot of fields, including finance and economics. As a major dimension reduction tool, similar to its factor model counterpart, the idi…
On testing for high-dimensional white noise
Zeng Li, Clifford Lam, Jianfeng Yao +1
Testing for white noise is a classical yet important problem in statistics, especially for diagnostic checks in time series modeling and linear regression. For high-dimensional tim…
Factor modeling for high-dimensional time series: Inference for the number of factors
Clifford Lam, Qiwei Yao
This paper deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. Under stationary settings, the inference is simple in the sense…
Estimation for Latent Factor Models for High-Dimensional Time Series
Clifford Lam, Qiwei Yao, Neil Bathia
This paper deals with the dimension reduction for high-dimensional time series based on common factors. In particular we allow the dimension of time series to be as large as, o…
Estimation of Large Precision Matrices Through Block Penalization
Clifford Lam
This paper focuses on exploring the sparsity of the inverse covariance matrix $\bSigma^{-1}$, or the precision matrix. We form blocks of parameters based on each off-diagonal band…
Sparsistency and rates of convergence in large covariance matrix estimation
Clifford Lam, Jianqing Fan
This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with nonconvex penalty functions.…
Profile-Kernel likelihood inference with diverging number of parameters
C. Lam, J. Fan
The generalized varying coefficient partially linear model with growing number of predictors arises in many contemporary scientific endeavor. In this paper we set foot on both theo…