14 citations · 18 across the 14 of their papers we have counts for
15 papers
Generalized Spectral Testing with Sample Splitting
Yuxin Tao, Feiyu Jiang, Xiaofeng Shao
Residual-based goodness-of-fit tests for parametric time-series models are often complicated by parameter-estimation effects, which can alter the limiting behavior of diagnostic st…
Diagnostic Checking for Wasserstein Autoregression
Chenxiao Dai, Feiyu Jiang, Dong Li +1
Wasserstein autoregression provides a robust framework for modeling serial dependence among probability distributions, with wide-ranging applications in economics, finance, and cli…
Estimation for conditional moment models based on martingale difference divergence
Kunyang Song, Feiyu Jiang, Ke Zhu
We provide a new estimation method for conditional moment models via the martingale difference divergence (MDD).Our MDD-based estimation method is formed in the framework of a cont…
SNSeg: An R Package for Time Series Segmentation via Self-Normalization
Shubo Sun, Zifeng Zhao, Feiyu Jiang +1
Time series segmentation aims to identify potential change-points in a sequence of temporally dependent data, so that the original sequence can be partitioned into several homogene…
Model-free Change-point Detection using AUC of a Classifier
Rohit Kanrar, Feiyu Jiang, Zhanrui Cai
In contemporary data analysis, it is increasingly common to work with non-stationary complex data sets. These data sets typically extend beyond the classical low-dimensional Euclid…
Two-Sample and Change-Point Inference for Non-Euclidean Valued Time Series
Feiyu Jiang, Changbo Zhu, Xiaofeng Shao
Data objects taking value in a general metric space have become increasingly common in modern data analysis. In this paper, we study two important statistical inference problems, n…
Matrix GARCH Model: Inference and Application
Cheng Yu, Dong Li, Feiyu Jiang +1
Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in eco…
High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection
Zifeng Zhao, Feiyu Jiang, Yi Yu +1
We consider a high-dimensional dynamic pricing problem under non-stationarity, where a firm sells products to sequentially arriving consumers that behave according to an unknow…
Testing Serial Independence of Object-Valued Time Series
Feiyu Jiang, Hanjia Gao, Xiaofeng Shao
We propose a novel method for testing serial independence of object-valued time series in metric spaces, which is more general than Euclidean or Hilbert spaces. The proposed method…
A Consistent ICM-based Specification Test
Feiyu Jiang, Emmanuel Selorm Tsyawo
In spite of the omnibus property of Integrated Conditional Moment (ICM) specification tests, they are not commonly used in empirical practice owing to features such as the non-pivo…
Robust Inference for Change Points in High Dimension
Feiyu Jiang, Runmin Wang, Xiaofeng Shao
This paper proposes a new test for a change point in the mean of high-dimensional data based on the spatial sign and self-normalization. The test is easy to implement with no tunin…
Time Series Analysis of COVID-19 Infection Curve: A Change-Point Perspective
Feiyu Jiang, Zifeng Zhao, Xiaofeng Shao
In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the…
Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model
Feiyu Jiang, Dong Li, Ke Zhu
This paper considers a semiparametric generalized autoregressive conditional heteroskedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run compone…
Non-standard inference for augmented double autoregressive models with null volatility coefficients
Feiyu Jiang, Dong Li, Ke Zhu
This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Sin…
Time series models for realized covariance matrices based on the matrix-F distribution
Jiayuan Zhou, Feiyu Jiang, Ke Zhu +1
We propose a new Conditional BEKK matrix-F (CBF) model for the time-varying realized covariance (RCOV) matrices. This CBF model is capable of capturing heavy-tailed RCOV, which is…