Copertina

Robust Quickest Change Detection in Multi-Stream Non-Stationary Processes

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hou, Yingze, Bidkhori, Hoda, Banerjee, Taposh
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Methodology
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Robust Quickest Change Detection with Sampling Control
di: Hou, Yingze, et al.
Pubblicazione: (2024)

Robust Quickest Change Detection in Non-Stationary Processes
di: Hou, Yingze, et al.
Pubblicazione: (2023)

Score-Based Quickest Change Detection and Fault Identification for Multi-Stream Signals
di: Chen, Wuxia, et al.
Pubblicazione: (2025)

Robust Quickest Correlation Change Detection in High-Dimensional Random Vectors
di: Alghamdi, Assma, et al.
Pubblicazione: (2024)

Robust Score-Based Quickest Change Detection
di: Moushegian, Sean, et al.
Pubblicazione: (2024)

Quickest Change Detection with Cost-Constrained Experiment Design
di: Lubenia, Patrick Vincent N., et al.
Pubblicazione: (2025)

Conditional Score Learning for Quickest Change Detection in Markov Transition Kernels
di: Chen, Wuxia, et al.
Pubblicazione: (2025)

On Multi-entity, Multivariate Quickest Change Point Detection
di: Kor, Bahar, et al.
Pubblicazione: (2025)

Large Deviation Analysis of Score-based Hypothesis Testing
di: Diao, Enmao, et al.
Pubblicazione: (2024)

Copula-Based Time Series for Non-Gaussian and Non-Markovian Stationary Processes
di: Pappert, Sven, et al.
Pubblicazione: (2026)

Likelihood Inference for Possibly Non-Stationary Processes via Adaptive Overdifferencing
di: Griffin, Maryclare, et al.
Pubblicazione: (2020)

Information-Enriched Selection of Stationary and Non-Stationary Autoregressions using the Adaptive Lasso
di: Reinschlüssel, Thilo, et al.
Pubblicazione: (2024)

Accounting for Multiple Covariates in Non-Stationary Geostatistical Modelling
di: Johnson, Olatunji, et al.
Pubblicazione: (2024)

Approximately Optimal Multi-Stream Quickest Change Detection
di: Kartzman, Joshua, et al.
Pubblicazione: (2026)

Change Point Detection for Functional Autoregressive Processes on the Sphere
di: Spoto, Federica, et al.
Pubblicazione: (2025)

Active Learning with Adaptive Non-Stationary Kernel for Continuous-Fidelity Surrogate Models
di: Boutelet, Romain, et al.
Pubblicazione: (2025)

Inference for Multiple Change-points in Piecewise Locally Stationary Time Series
di: Ng, Wai Leong, et al.
Pubblicazione: (2026)

Bayesian Inference for Stationary Points in Gaussian Process Regression Models for Event-Related Potentials Analysis
di: Yu, Cheng-Han, et al.
Pubblicazione: (2020)

FedGTEA: Federated Class-Incremental Learning with Gaussian Task Embedding and Alignment
di: Li, Haolin, et al.
Pubblicazione: (2025)

On Statistical Inference for Rates of Change in Spatial Processes over Riemannian Manifolds
di: Li, Didong, et al.
Pubblicazione: (2026)

Change-point problem: Direct estimation using a geometry inspired identifiable reparameterization
di: Banerjee, Buddhananda, et al.
Pubblicazione: (2025)

Comparison of Estimators for Multi-State Models in Potentially Non-Markov Processes
di: Drenda, Carolin, et al.
Pubblicazione: (2025)

The Poisson-Gaussian Mixture Process: A Flexible and Robust Approach for Non-Gaussian Geostatistical Modeling
di: Gonçalves, F. B., et al.
Pubblicazione: (2022)

Multi-Rank Subspace Change-Point Detection for Monitoring Robotic Swarms
di: Lee, Jonghyeok, et al.
Pubblicazione: (2025)

Scalable and Robust Spatial Prediction via Multi-Resolution Ensembles of Predictive Processes
di: Bianco, Nicolas, et al.
Pubblicazione: (2026)

Simultaneous Inference for Non-Stationary Random Fields, with Application to Gridded Data Analysis
di: Zhang, Yunyi, et al.
Pubblicazione: (2024)

Validation and Comparison of Non-Stationary Cognitive Models: A Diffusion Model Application
di: Schumacher, Lukas, et al.
Pubblicazione: (2023)

Stationary subspace analysis for spatial data
di: Saarela, Perttu, et al.
Pubblicazione: (2026)

Network Estimation for Stationary Time Series
di: Shelley, Madeline A., et al.
Pubblicazione: (2025)

Self-Normalization for CUSUM-based Change Detection in Locally Stationary Time Series
di: Heinrichs, Florian
Pubblicazione: (2025)

Identification of Emotionally Stressful Periods Through Tracking Changes in Statistical Features of mHealth Data
di: Kim, Younghoon, et al.
Pubblicazione: (2025)

Bernstein Polynomial Processes for Continuous Time Change Detection
di: Cunha, Dan, et al.
Pubblicazione: (2025)

Sequential Outlier Detection in Non-Stationary Time Series
di: Heinrichs, Florian, et al.
Pubblicazione: (2025)

Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels
di: Coube-Sisqueille, Sébastien, et al.
Pubblicazione: (2022)

Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection
di: Gao, Daiqi, et al.
Pubblicazione: (2023)

Stationary Point Constrained Inference via Diffeomorphisms
di: Price, Michael, et al.
Pubblicazione: (2025)

The Bayesian Gaussian Process Latent Variable Model for Spatio-Temporal Stream Networks
di: Basson, Marno, et al.
Pubblicazione: (2026)

Diffusion-Based Hypothesis Testing and Change-Point Detection
di: Moushegian, Sean, et al.
Pubblicazione: (2025)

Detecting Spatial Health Disparities Using Disease Maps
di: Aiello, Luca, et al.
Pubblicazione: (2023)

Bootstrap-based Inference for Bivariate Heteroscedastic Extremes with a Changing Tail Copula
di: Hu, Yifan, et al.
Pubblicazione: (2024)