Copertina

A Graph-based Approach to Estimating the Number of Clusters in High-dimensional Settings

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Bai, Yichuan, Chu, Lynna
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Methodology
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Graph-theoretic Inference for Random Effects in High-dimensional Studies
di: Chu, Lynna, et al.
Pubblicazione: (2025)

A Robust Framework for Graph-based Two-Sample Tests Using Weights
di: Bai, Yichuan, et al.
Pubblicazione: (2023)

Adaptive Block-Based Change-Point Detection for Sparse Spatially Clustered Data with Applications in Remote Sensing Imaging
di: Moore, Alan, et al.
Pubblicazione: (2025)

A Doubled Adjacency Spectral Embedding Approach to Graph Clustering
di: Park, Sinyoung, et al.
Pubblicazione: (2025)

Inference for Clustering: Conformal Sets for Cluster Labels
di: Hur, YoonHaeng, et al.
Pubblicazione: (2026)

Synthetic Heterogeneous-Effects LASSO: A Fixed-effects Estimation Approach for High-dimensional Mixed-effects Models
di: Ye, Shangyuan, et al.
Pubblicazione: (2026)

Robust Clustered Federated Learning for Heterogeneous High-dimensional Data
di: Yang, Changxin, et al.
Pubblicazione: (2025)

Comparative Review of Modern Competing Risk Methods in High-dimensional Settings
di: Djangang, Paul M., et al.
Pubblicazione: (2025)

High-Dimensional Multivariate VAR Estimation with Spatio-Temporal Structure
di: Bai, Peiliang
Pubblicazione: (2026)

Estimation of the Number Needed to Treat, the Number Needed to be Exposed, and the Exposure Impact Number with Instrumental Variables
di: Vancak, Valentin, et al.
Pubblicazione: (2023)

Robust and Well-conditioned Sparse Estimation for High-dimensional Covariance Matrices
di: Wang, Shaoxin, et al.
Pubblicazione: (2025)

A Latent Variable Approach to Learning High-dimensional Multivariate longitudinal Data
di: Lee, Sze Ming, et al.
Pubblicazione: (2024)

The Effective Number of Parameters in Kernel Density Estimation
di: Guglielmini, Sofia, et al.
Pubblicazione: (2024)

Conformal Prediction Sets for Populations of Graphs
di: Calissano, Anna, et al.
Pubblicazione: (2024)

OPTICS: Order-Preserved Test-Inverse Confidence Set for Number of Change-Points
di: Sun, Ao, et al.
Pubblicazione: (2026)

Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation
di: Dirmeier, Simon, et al.
Pubblicazione: (2023)

High-dimensional Covariance Estimation by Pairwise Likelihood Truncation
di: Casa, Alessandro, et al.
Pubblicazione: (2024)

Change Point Detection for High-dimensional Linear Models: A General Tail-adaptive Approach
di: Liu, Bin, et al.
Pubblicazione: (2022)

Spatial-Sign based High dimensional Change Point Inference
di: Liu, Jixuan, et al.
Pubblicazione: (2025)

Skeleton Regression: A Graph-Based Approach to Estimation with Manifold Structure
di: Wei, Zeyu, et al.
Pubblicazione: (2023)

Feature Selection in High-dimensional Spaces Using Graph-Based Methods
di: Ghosh, Swarnadip, et al.
Pubblicazione: (2021)

Bayesian Level Set Clustering
di: Buch, David, et al.
Pubblicazione: (2024)

Definition, Identification, and Estimation of the Direct and Indirect Number Needed to Treat
di: Vancak, Valentin, et al.
Pubblicazione: (2025)

From Likelihood to Limit State: A Reliability-Inspired Framework for Bayesian Evidence Estimation and High-dimensional Sampling
di: Liao, Zihan, et al.
Pubblicazione: (2024)

Linear Regression: Inference Based on Cluster Estimates
di: Dey, Subhodeep, et al.
Pubblicazione: (2026)

A Scalable Variational Bayes Approach to Fit High-dimensional Spatial Generalized Linear Mixed Models
di: Lee, Jin Hyung, et al.
Pubblicazione: (2024)

A General Latent Embedding Approach for Modeling Non-uniform High-dimensional Sparse Hypergraphs with Multiplicity
di: Wu, Shihao, et al.
Pubblicazione: (2024)

A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects
di: Bo, Shi, et al.
Pubblicazione: (2024)

A Recursive Partitioning Approach for Dynamic Discrete Choice Modeling in High Dimensional Settings
di: Barzegary, Ebrahim, et al.
Pubblicazione: (2022)

Estimation of High-dimensional Nonlinear Vector Autoregressive Models
di: Han, Yuefeng, et al.
Pubblicazione: (2025)

Clustering Tails in High Dimension
di: Chen, Liujun, et al.
Pubblicazione: (2025)

Model-based Clustering
di: Grün, Bettina
Pubblicazione: (2018)

Graph-based Square-Root Estimation for Sparse Linear Regression
di: Li, Peili, et al.
Pubblicazione: (2024)

Dirichlet Process-based Robust Clustering using the Median-of-Means Estimator
di: Basu, Supratik, et al.
Pubblicazione: (2023)

A Primal Dual Active Set with Continuation Algorithm for $\ell_0$-Penalized High-dimensional Accelerated Failure Time Model
di: Li, Peili, et al.
Pubblicazione: (2025)

An Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization
di: Hu, Shouri, et al.
Pubblicazione: (2024)

Estimating Signal-to-Noise Ratios for Multivariate High-dimensional Linear Models
di: Hu, Xiaohan, et al.
Pubblicazione: (2025)

High-dimensional copula-based Wasserstein dependence
di: De Keyser, Steven, et al.
Pubblicazione: (2024)

Decoder-only Clustering in Attributed Graphs
di: Kei, Yik Lun, et al.
Pubblicazione: (2025)

Bayesian Learning of Relational Graph in Semiparametric High-dimensional Time Series
di: Roy, Arkaprava, et al.
Pubblicazione: (2024)