On a clustering criterion for dependent observations

DOI10.1016/j.jspi.2013.11.005zbMath1432.62178arXiv1311.3998OpenAlexW2962967416MaRDI QIDQ389296

Karthik Bharath

Publication date: 20 January 2014

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1311.3998

zbMATH Keywords

\(\beta\)-mixingclusteringCLTfunctional CLTLLNtruncated sums

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Central limit and other weak theorems (60F05) Stationary stochastic processes (60G10) Functional limit theorems; invariance principles (60F17)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Asymptotic distributions for clustering criteria
• Central limit theorems for empirical and \(U\)-processes of stationary mixing sequences
• The functional central limit theorem for strongly mixing processes
• Mixing: Properties and examples
• Rates of convergence for empirical processes of stationary mixing sequences
• Learning and generalisation. With applications to neural networks.
• Support-vector networks
• Asymptotics of a clustering criterion for smooth distributions
• Asymptotic theory of weakly dependent stochastic processes
• Asymptotic Statistics
• Convergence of stochastic processes

This page was built for publication: On a clustering criterion for dependent observations