Zobrazeno 1 - 10
of 251 778
pro vyhledávání: '"Méthodology"'
Autor:
Yafeng Shan, Jon Williamson
This volume contends that Evidential Pluralism—an account of the epistemology of causation, which maintains that in order to establish a causal claim one needs to establish the existence of a correlation and the existence of a mechanism—can be fr
Time-varying random objects have been increasingly encountered in modern data analysis. Moreover, in a substantial number of these applications, periodic behavior of the random objects has been observed. We introduce a new, powerful scan statistic an
Externí odkaz:
http://arxiv.org/abs/2501.01657
Inference in matrix-valued time series with common stochastic trends and multifactor error structure
We develop an estimation methodology for a factor model for high-dimensional matrix-valued time series, where common stochastic trends and common stationary factors can be present. We study, in particular, the estimation of (row and column) loading s
Externí odkaz:
http://arxiv.org/abs/2501.01925
Autor:
Cui, Fuheng, Walker, Stephen G.
Uncertainty associated with statistical problems arises due to what has not been seen as opposed to what has been seen. Using probability to quantify the uncertainty the task is to construct a probability model for what has not been seen conditional
Externí odkaz:
http://arxiv.org/abs/2501.01890
We propose multiplier bootstrap procedures for nonparametric inference and uncertainty quantification of the target mean function, based on a novel framework of integrating target and source data. We begin with the relatively easier covariate shift s
Externí odkaz:
http://arxiv.org/abs/2501.01610
Hypergraph data, which capture multi-way interactions among entities, are becoming increasingly prevalent in the big data eta. Generating new hyperlinks from an observed, usually high-dimensional hypergraph is an important yet challenging task with d
Externí odkaz:
http://arxiv.org/abs/2501.01541
Respondent-driven sampling (RDS) is widely used to study hidden or hard-to-reach populations by incentivizing study participants to recruit their social connections. The success and efficiency of RDS can depend critically on the nature of the incenti
Externí odkaz:
http://arxiv.org/abs/2501.01505
Autor:
Lee, Seunghyun, Gu, Yuqi
In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statistical properties of these models remain underexplored. DGMs are often
Externí odkaz:
http://arxiv.org/abs/2501.01414
In this work, we develop a scalable approach for a flexible latent factor model for high-dimensional dynamical systems. Each latent factor process has its own correlation and variance parameters, and the orthogonal factor loading matrix can be either
Externí odkaz:
http://arxiv.org/abs/2501.01324
Semiconductor nano-crystals, known as quantum dots (QDs), have garnered significant interest in various scientific fields due to their unique fluorescence properties. One captivating characteristic of QDs is their ability to emit photons under contin
Externí odkaz:
http://arxiv.org/abs/2501.01292