Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition
From MaRDI portal
Publication:6622420
DOI10.1080/00401706.2017.1346522MaRDI QIDQ6622420
Jianjun Shi, Kamran Paynabar, Hao Yan
Publication date: 22 October 2024
Published in: Technometrics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Efficient scalable schemes for monitoring a large number of data streams
- Least angle regression. (With discussion)
- Multivariate Statistical Process Control Using LASSO
- Control Charts for Multivariate Processes
- Statistical process control for monitoring non-linear profiles using wavelet filtering and B-Spline approximation
- Uniqueness of the Gaussian Kernel for Scale-Space Filtering
- Nonparametric regression analysis of multivariate longitudinal data
- Fast BivariateP-Splines: The Sandwich Smoother
Related Items (15)
Imputed mean tensor regression for near-sited spatial temporal data ⋮ Dynamic modeling and online monitoring of tensor data streams with application to passenger flow surveillance ⋮ Image-Based Prognostics Using Penalized Tensor Regression ⋮ Spatial Signal Detection Using Continuous Shrinkage Priors ⋮ Super Resolution for Multi-Sources Image Stream Data Using Smooth and Sparse Tensor Completion and Its Applications in Data Acquisition of Additive Manufacturing ⋮ An Adaptive Sampling Strategy for Online Monitoring and Diagnosis of High-Dimensional Streaming Data ⋮ Reliable Post-Signal Fault Diagnosis for Correlated High-Dimensional Data Streams ⋮ Adaptive Partially Observed Sequential Change Detection and Isolation ⋮ A Bayesian Partially Observable Online Change Detection Approach with Thompson Sampling ⋮ Joint Diagnosis of High-Dimensional Process Mean and Covariance Matrix based on Bayesian Model Selection ⋮ Beyond symmetry: best submatrix selection for the sparse truncated SVD ⋮ A Diagnostic Procedure for High-Dimensional Data Streams via Missed Discovery Rate Control ⋮ An Adaptive Sampling Strategy for Real-Time Anomaly Detection with Unmanned Sensing Vehicles ⋮ Statistical Process Monitoring from Industry 2.0 to Industry 4.0: Insights into Research and Practice ⋮ Detection of Emergent Anomalous Structure in Functional Data
This page was built for publication: Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition
