92 citations · 185 across the 21 of their papers we have counts for
22 papers
Quantile Vector Autoregression without Crossing
Tomohiro Ando, Tadao Hoshino, Ruey Tsay
This paper considers estimation and model selection of quantile vector autoregression (QVAR). Conventional quantile regression often yields undesirable crossing quantile curves, vi…
Model Selection for Unit-root Time Series with Many Predictors
Shuo-Chieh Huang, Ching-Kang Ing, Ruey S. Tsay
This paper studies model selection for general unit-root time series, including the case with many exogenous predictors. We propose a new model selection algorithm, FHTD, that leve…
Temporal Wasserstein Imputation: A Versatile Method for Time Series Imputation
Shuo-Chieh Huang, Tengyuan Liang, Ruey S. Tsay
Missing data can significantly hamper standard time series analysis, yet they occur frequently in applications. In this paper, we introduce temporal Wasserstein imputation, a novel…
Vector AutoRegressive Moving Average Models: A Review
Marie-Christine Düker, David S. Matteson, Ruey S. Tsay +1
Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vect…
Time Series Forecasting with Many Predictors
Shuo-Chieh Huang, Ruey S. Tsay
We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as…
Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors
Zhaoxing Gao, Ruey S. Tsay
This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised…
Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data
Shuo-Chieh Huang, Ruey S. Tsay
Feature-distributed data, referred to data partitioned by features and stored across multiple computing nodes, are increasingly common in applications with a large number of featur…
High-Dimensional Vector Autoregression with Common Response and Predictor Factors
Di Wang, Xiaoyu Zhang, Guodong Li +1
The reduced-rank vector autoregressive (VAR) model can be interpreted as a supervised factor model, where two factor modelings are simultaneously applied to response and predictor…
Rate-Optimal Robust Estimation of High-Dimensional Vector Autoregressive Models
Di Wang, Ruey S. Tsay
High-dimensional time series data appear in many scientific areas in the current data-rich environment. Analysis of such data poses new challenges to data analysts because of not o…
Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data
Zhaoxing Gao, Ruey S. Tsay
This paper proposes a hierarchical approximate-factor approach to analyzing high-dimensional, large-scale heterogeneous time series data using distributed computing. The new method…
A Two-Way Transformed Factor Model for Matrix-Variate Time Series
Zhaoxing Gao, Ruey S. Tsay
We propose a new framework for modeling high-dimensional matrix-variate time series by a two-way transformation, where the transformed data consist of a matrix-variate factor proce…
Modeling High-Dimensional Unit-Root Time Series
Zhaoxing Gao, Ruey S. Tsay
This paper proposes a new procedure to build factor models for high-dimensional unit-root time series by postulating that a -dimensional unit-root process is a nonsingular linea…
Tensor Canonical Correlation Analysis with Convergence and Statistical Guarantees
You-Lin Chen, Mladen Kolar, Ruey S. Tsay
In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of an ad-hoc way of transforming data to vectors…
Modeling High-Dimensional Time Series: A Factor Model with Dynamically Dependent Factors and Diverging Eigenvalues
Zhaoxing Gao, Ruey S. Tsay
This article proposes a new approach to modeling high-dimensional time series by treating a -dimensional time series as a nonsingular linear transformation of certain common fac…
A Structural-Factor Approach to Modeling High-Dimensional Time Series and Space-Time Data
Zhaoxing Gao, Ruey S Tsay
This paper considers a structural-factor approach to modeling high-dimensional time series and space-time data by decomposing individual series into trend, seasonal, and irregular…
Spatio-temporal models with space-time interaction and their applications to air pollution data
Soudeep Deb, Ruey S. Tsay
It is of utmost importance to have a clear understanding of the status of air pollution and to provide forecasts and insights about the air quality to the general public and resear…
Constrained Factor Models for High-Dimensional Matrix-Variate Time Series
Elynn Y. Chen, Ruey S. Tsay, Rong Chen
High-dimensional matrix-variate time series data are becoming widely available in many scientific fields, such as economics, biology, and meteorology. To achieve significant dimens…
High-dimensional Linear Regression for Dependent Data with Applications to Nowcasting
Yuefeng Han, Ruey S. Tsay
Recent research has focused on penalized least squares (Lasso) estimators for high-dimensional linear regressions in which the number of covariates is considerably lar…
Independent Component Analysis via Distance Covariance
David S. Matteson, Ruey S. Tsay
This paper introduces a novel statistical framework for independent component analysis (ICA) of multivariate data. We propose methodology for estimating and testing the existence o…
Discussion of "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong
Kung-Sik Chan, Ruey S. Tsay
Discussion of "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong [arXiv:1104.3073]
A Conversation with George C. Tiao
Daniel Peña, Ruey S. Tsay
George C. Tiao was born in London in 1933. After graduating with a B.A. in Economics from National Taiwan University in 1955 he went to the US to obtain an M.B.A from New York Univ…
Multivariate volatility models
Ruey S. Tsay
Correlations between asset returns are important in many financial applications. In recent years, multivariate volatility models have been used to describe the time-varying feature…