Results 11 to 20 of about 32,006,081 (306)
Co-clustering of Time-Dependent Data via the Shape Invariant Model. [PDF]
J Classif, 2021 Multivariate time-dependent data, where multiple features are observed over time for a set of individuals, are increasingly widespread in many application domains.
Casa A +3 more
europepmc +3 more sources
Uniform convergence of estimator for nonparametric regression with dependent data
Journal of Inequalities and Applications, 2016 In this paper, the authors investigate the internal estimator of nonparametric regression with dependent data such as α-mixing. Under suitable conditions such as the arithmetically α-mixing and E | Y 1 | s < ∞ $E|Y_{1}|^{s} 2 $s>2$ ), the convergence ...
Xiaoqin Li, Wenzhi Yang, Shuhe Hu
doaj +2 more sources
Time-dependent Data-driven Modeling of Active Region Evolution Using Energy-optimized Photospheric Electric Fields. [PDF]
Sol Phys, 2019 In this work, we present results of a time-dependent data-driven numerical simulation developed to study the dynamics of coronal active region magnetic fields.
Pomoell J, Lumme E, Kilpua E.
europepmc +2 more sources
Data Sampling Affects the Complexity of Online SGD over Dependent Data [PDF]
Conference on Uncertainty in Artificial Intelligence, 2022 Conventional machine learning applications typically assume that data samples are independently and identically distributed (i.i.d.). However, practical scenarios often involve a data-generating process that produces highly dependent data samples, which ...
Shaocong Ma +4 more
semanticscholar +1 more source
Central limit theorems for high dimensional dependent data [PDF]
Bernoulli, 2021 Motivated by statistical inference problems in high-dimensional time series data analysis, we first derive non-asymptotic error bounds for Gaussian approximations of sums of high-dimensional dependent random vectors on hyper-rectangles, simple convex ...
Jinyuan Chang, Xiaohui Chen, Mingcong Wu
semanticscholar +1 more source
Random Forests for Spatially Dependent Data
Journal of the American Statistical Association, 2021 Spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process (GP) distributed spatial random effect, are widely used for analyses of geospatial data. We consider the setting where the covariate effect is nonlinear.
Arkajyoti Saha, Sumanta Basu, A. Datta
semanticscholar +1 more source
Towards a Robust Approach to Analyze Time-Dependent Data in Software Engineering
IEEE International Conference on Software Analysis, Evolution, and Reengineering, 2022 Background. Several recent software engineering studies use data mined from the version control systems adopted by the different software projects. However, inspecting the data and statistical methods used in those studies reveals several problems with ...
Nyyti Saarimäki +4 more
semanticscholar +1 more source
On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data [PDF]
Bernoulli, 2020 A regression problem with dependent data is considered. Regularity assumptions on the dependency of the data are introduced, and it is shown that under suitable structural assumptions on the regression function a deep recurrent neural network estimate is
M. Kohler, A. Krzyżak
semanticscholar +1 more source
Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data [PDF]
European Conference on Artificial Intelligence, 2019 Spatial and time-dependent data is of interest in many applications. This task is difficult due to its complex spatial dependency, long-range temporal dependency, data non-stationarity, and data heterogeneity.
Y. Li, J. Moura
semanticscholar +1 more source
Astronomy & Astrophysics, 2020 Aims. We present a detailed examination of the magnetic evolution of AR 12473 using time-dependent, data-driven magnetofrictional modelling. Methods. We used maps of the photospheric electric field inverted from vector magnetogram observations, obtained ...
D. Price, J. Pomoell, E. Kilpua
semanticscholar +1 more source