Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Chang, Jinyuan"'
We investigate the identification and the estimation for matrix time series CP-factor models. Unlike the generalized eigenanalysis-based method of Chang et al. (2023) which requires the two factor loading matrices to be full-ranked, the newly propose
Externí odkaz:
http://arxiv.org/abs/2410.05634
We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series $p$ is large in relation to the length of time series $n$. Our first step performs an eigenanalysis of a pos
Externí odkaz:
http://arxiv.org/abs/2406.00700
We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses the models which accommodate, for example, transitivity, density-dependent and other stylized features often observed in real network data. By
Externí odkaz:
http://arxiv.org/abs/2404.15654
We introduce an ordinary differential equation (ODE) based deep generative method for learning conditional distributions, named Conditional F\"ollmer Flow. Starting from a standard Gaussian distribution, the proposed flow could approximate the target
Externí odkaz:
http://arxiv.org/abs/2402.01460
Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures. In this paper, we propose rigorous statistical testing procedures for making
Externí odkaz:
http://arxiv.org/abs/2303.03582
In this study, we investigate the performance of the Metropolis-adjusted Langevin algorithm in a setting with constraints on the support of the target distribution. We provide a rigorous analysis of the resulting Markov chain, establishing its conver
Externí odkaz:
http://arxiv.org/abs/2302.11971
The spectral density matrix is a fundamental object of interest in time series analysis, and it encodes both contemporary and dynamic linear relationships between component processes of the multivariate system. In this paper we develop novel inferenc
Externí odkaz:
http://arxiv.org/abs/2212.13686
Publikováno v:
Journal of Econometrics 2023, Vol. 235, pp. 972-1000
In this paper, we consider testing the martingale difference hypothesis for high-dimensional time series. Our test is built on the sum of squares of the element-wise max-norm of the proposed matrix-valued nonlinear dependence measure at different lag
Externí odkaz:
http://arxiv.org/abs/2209.04770
Publikováno v:
Journal of the Royal Statistical Society Series B 2023, Vol. 85, pp. 127-148
We consider to model matrix time series based on a tensor CP-decomposition. Instead of using an iterative algorithm which is the standard practice for estimating CP-decompositions, we propose a new and one-pass estimation procedure based on a general
Externí odkaz:
http://arxiv.org/abs/2112.15423
Publikováno v:
Annals of Statistics 2024, Vol. 52, pp. 708-728
A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here we conduct an in-depth study of this trade-off for parameter estimation in the $\beta$-model (Chatterjee, Diaconis an
Externí odkaz:
http://arxiv.org/abs/2112.10151