Zobrazeno 1 - 10
of 595
pro vyhledávání: '"Tsay, Ruey"'
Searching for new effective risk factors on stock returns is an important research topic in asset pricing. Factor modeling is an active research topic in statistics and econometrics, with many new advances. However, these new methods have not been fu
Externí odkaz:
http://arxiv.org/abs/2409.17182
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 Vector AutoRegressive (VAR) models, their popularity in empirical applicat
Externí odkaz:
http://arxiv.org/abs/2406.19702
Autor:
Huang, Shuo-Chieh, Tsay, Ruey S.
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 the group orthogonal greedy algorithm and the high-dimensional Akaike
Externí odkaz:
http://arxiv.org/abs/2406.09625
Autor:
Gao, Zhaoxing, Tsay, Ruey S.
Ridge regression is an indispensable tool in big data analysis. Yet its inherent bias poses a significant and longstanding challenge, compromising both statistical efficiency and scalability across various applications. To tackle this critical issue,
Externí odkaz:
http://arxiv.org/abs/2405.00424
Autor:
Gao, Zhaoxing, Tsay, Ruey S.
This paper proposes a new multilinear projection method for dimension-reduction in modeling high-dimensional matrix-variate time series. It assumes that a $p_1\times p_2$ matrix-variate time series consists of a dynamically dependent, lower-dimension
Externí odkaz:
http://arxiv.org/abs/2309.02674
Autor:
Gao, Zhaoxing, Tsay, Ruey S.
Publikováno v:
Journal of the American Statistical Association, 2024
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 PCA provides an effective way to bridge the gap between predictors an
Externí odkaz:
http://arxiv.org/abs/2307.07689
Autor:
Huang, Shuo-Chieh, Tsay, Ruey S.
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 features. This paper proposes a two-stage relaxed greedy algorithm (TSRGA) f
Externí odkaz:
http://arxiv.org/abs/2307.03410
This paper proposes a new approach to identifying the effective cointegration rank in high-dimensional unit-root (HDUR) time series from a prediction perspective using reduced-rank regression. For a HDUR process $\mathbf{x}_t\in \mathbb{R}^N$ and a s
Externí odkaz:
http://arxiv.org/abs/2304.12134