Kernel Estimators for Distribution Functions on Dependent Random Fields

DOI10.1080/03610920802641717zbMath1179.62050OpenAlexW1969916639MaRDI QIDQ3652782

Jiexiang Li

Publication date: 16 December 2009

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920802641717

zbMATH Keywords

random fielddistribution functionmixing conditions

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Estimation in survival analysis and censored data (62N02)

Cites Work

• Kernel density estimation on random fields
• Kernel density estimation for random fields. (Density estimation for random fields)
• A note on empirical processes of strong-mixing sequences
• Relative efficiency and deficiency of kernel type estimators of smooth distribution functions
• Conditions for linear processes to be strong-mixing