Spatiotemporal Heterogeneity Learning: Generalized SpatioTemporal Semi-Varying Coefficient Models With Structure Identification [0.03%]
时空异质性学习：带结构识别的广义时空半可变系数模型
Zhiling Gu,Xinyi Li,Guannan Wang et al.
Zhiling Gu et al.
This paper proposes a class of Generalized SpatioTemporal Semi-Varying Coefficient Models (GST-SVCMs) with structure identification to enhance the detection and interpretation of spatiotemporal heterogeneity in factors influencing response ...
On the Sensitivity of Granger Causality to Errors-In-Variables, Linear Transformations and Subsampling [0.03%]
误差变量、线性变换和子采样对格兰杰因果关系的影响
Brian D O Anderson,Manfred Deistler,Jean-Marie Dufour
Brian D O Anderson
This article studies the sensitivity of Granger causality to the addition of noise, the introduction of subsampling, and the application of causal invertible filters to weakly stationary processes. Using canonical spectral factors and Wold ...
Extracting Conditionally Heteroskedastic Components using Independent Component Analysis [0.03%]
基于独立成分分析提取条件异方差性组件
Jari Miettinen,Markus Matilainen,Klaus Nordhausen et al.
Jari Miettinen et al.
In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we s...
Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability [0.03%]
存在组内谱变异的时间序列判别分析
Robert T Krafty
Robert T Krafty
Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time serie...
Zhibiao Zhao,Yiyun Zhang,Runze Li
Zhibiao Zhao
We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-seq...
Enveloping Spectral Surfaces: Covariate Dependent Spectral Analysis of Categorical Time Series [0.03%]
enveloping谱包络表面：基于分类时间序列的协变量依赖谱分析
Robert T Krafty,Shuangyan Xiong,David S Stoffer et al.
Robert T Krafty et al.
Motivated by problems in Sleep Medicine and Circadian Biology, we present a method for the analysis of cross-sectional categorical time series collected from multiple subjects where the effect of static continuous-valued covariates is of in...
Paul L Anderson,Mark M Meerschaert,Kai Zhang
Paul L Anderson
Periodic autoregressive moving average (PARMA) models are indicated for time series whose mean, variance, and covariance function vary with the season. In this paper, we develop and implement forecasting procedures for PARMA models. Forecas...
Autocovariance Structures for Radial Averages in Small Angle X-Ray Scattering Experiments [0.03%]
用于小角X射线散射实验径向平均自动共方差结构
F Jay Breidt,Andreea Erciulescu,Mark van der Woerd
F Jay Breidt
Small-angle X-ray scattering (SAXS) is a technique for obtaining low-resolution structural information about biological macromolecules, by exposing a dilute solution to a high-intensity X-ray beam and capturing the resulting scattering patt...
Daniel M Keenan,Xin Wang,Steven M Pincus et al.
Daniel M Keenan et al.
In most hormonal systems (as well as many physiological systems more generally), the chemical signals from the brain, which drive much of the dynamics, can not be observed in humans. By the time the molecules reach peripheral blood, they ha...