47 citations · 50 across the 11 of their papers we have counts for
12 papers
Distributionally Robust PCA with Data-Adaptive Wasserstein Geometry
Chuang Xu, Andrew T. A. Wood, Yanrong Yang
We develop a distributionally robust formulation of principal component analysis that minimizes worst-case reconstruction risk over distributions lying within a Wasserstein neighbo…
Uncertainty Learning for High-dimensional Mean-variance Portfolio
Ruike Wu, Yanrong Yang, Han Lin Shang +1
Robust estimation for modern portfolio selection on a large set of assets becomes more important due to large deviation of empirical inference on big data. We propose a distributio…
Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy
Jiti Gao, Fei Liu, Bin Peng +1
In this paper, we investigate a semiparametric regression model under the context of treatment effects via a localized neural network (LNN) approach. Due to a vast number of parame…
Homogeneity and Sub-homogeneity Pursuit: Iterative Complement Clustering PCA
Daning Bi, Le Chang, Yanrong Yang
Principal component analysis (PCA), the most popular dimension-reduction technique, has been used to analyze high-dimensional data in many areas. It discovers the homogeneity withi…
Spiked eigenvalues of high-dimensional sample autocovariance matrices: CLT and applications
Daning Bi, Xiao Han, Adam Nie +1
High-dimensional autocovariance matrices play an important role in dimension reduction for high-dimensional time series. In this article, we establish the central limit theorem (CL…
AR-sieve Bootstrap for High-dimensional Time Series
Daning Bi, Han Lin Shang, Yanrong Yang +1
This paper proposes a new AR-sieve bootstrap approach to high-dimensional time series. The major challenge of classical bootstrap methods on high-dimensional time series is two-fol…
Interactive Effects Panel Data Models with General Factors and Regressors
Bin Peng, Liangjun Su, Joakim Westerlund +1
This paper considers a model with general regressors and unobservable factors. An estimator based on iterated principal components is proposed, which is shown to be not only asympt…
Forecasting high-dimensional functional time series with dual-factor structures
Chen Tang, Han Lin Shang, Yanrong Yang +1
We propose a dual-factor model for high-dimensional functional time series (HDFTS) that considers multiple populations. The HDFTS is first decomposed into a collection of functiona…
A Forecast-driven Hierarchical Factor Model with Application to Mortality Data
Lingyu He, Fei Huang, Yanrong Yang
Mortality forecasting plays a pivotal role in insurance and financial risk management of life insurers, pension funds, and social securities. Mortality data is usually high-dimensi…
Estimation of Cross-Sectional Dependence in Large Panels
Jiti Gao, Guangming Pan, Yanrong Yang +1
Accurate estimation for extent of cross{sectional dependence in large panel data analysis is paramount to further statistical analysis on the data under study. Grouping more data w…
Independence test for high dimensional data based on regularized canonical correlation coefficients
Yanrong Yang, Guangming Pan
This paper proposes a new statistic to test independence between two high dimensional random vectors and . The proposed statistic…
High Dimensional Correlation Matrices: CLT and Its Applications
Jiti Gao, Xiao Han, Guangming Pan +1
Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, this paper establishes a new central limit theorem (CLT)…