Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Cai, Chencheng"'
Interference is ubiquitous when conducting causal experiments over networks. Except for certain network structures, causal inference on the network in the presence of interference is difficult due to the entanglement between the treatment assignments
Externí odkaz:
http://arxiv.org/abs/2312.04026
The progressive amplification of fluctuations in demand as the demand travels upstream the supply chains is known as the bullwhip effect. We first analytically characterize the bullwhip effect in general supply chain networks in two cases: (i) all su
Externí odkaz:
http://arxiv.org/abs/2208.04459
Randomized saturation designs are a family of designs which assign a possibly different treatment proportion to each cluster of a population at random. As a result, they generalize the well-known (stratified) completely randomized designs and the clu
Externí odkaz:
http://arxiv.org/abs/2203.09682
Publikováno v:
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2024
This paper introduces a general framework of Semi-parametric TEnsor Factor Analysis (STEFA) that focuses on the methodology and theory of low-rank tensor decomposition with auxiliary covariates. Semi-parametric TEnsor Factor Analysis models extend te
Externí odkaz:
http://arxiv.org/abs/2007.02404
Discovering the underlying low dimensional structure of high dimensional data has attracted a significant amount of researches recently and has shown to have a wide range of applications. As an effective dimension reduction tool, singular value decom
Externí odkaz:
http://arxiv.org/abs/1912.02955
We consider the problem of matrix approximation and denoising induced by the Kronecker product decomposition. Specifically, we propose to approximate a given matrix by the sum of a few Kronecker products of matrices, which we refer to as the Kronecke
Externí odkaz:
http://arxiv.org/abs/1912.02392
A matrix completion problem is to recover the missing entries in a partially observed matrix. Most of the existing matrix completion methods assume a low rank structure of the underlying complete matrix. In this paper, we introduce an alternative and
Externí odkaz:
http://arxiv.org/abs/1911.11774
Autor:
Cai, Chencheng, Chen, Rong
Many high dimensional optimization problems can be reformulated into a problem of finding theoptimal state path under an equivalent state space model setting. In this article, we present a general emulation strategy for developing a state space model
Externí odkaz:
http://arxiv.org/abs/1911.07172
Many massive data are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences and solutions, has the potential to create significa
Externí odkaz:
http://arxiv.org/abs/1906.05533
Sequential Monte Carlo (SMC) methods are a class of Monte Carlo methods that are used to obtain random samples of a high dimensional random variable in a sequential fashion. Many problems encountered in applications often involve different types of c
Externí odkaz:
http://arxiv.org/abs/1706.02348