Least-square estimation for regression on random designs for absolutely regular observations

DOI10.1016/S0167-7152(98)00221-1zbMath0933.62034OpenAlexW2041709792MaRDI QIDQ1284581

Gabrielle Viennet

Publication date: 2 April 2000

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(98)00221-1

zbMATH Keywords

sievesabsolute regularityminimum contrast estimationstrictly stationary sequencesleast-square regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Inference from stochastic processes (62M99)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Estimating a regression function
• The functional central limit theorem for strongly mixing processes
• Mixing: Properties and examples
• Covariance inequalities for strongly mixing processes
• Rates of convergence for minimum contrast estimators
• The use of polynomial splines and their tensor products in multivariate function estimation. (With discussion)
• Convergence rate of sieve estimates
• Inequalities for absolutely regular sequences: application to density estimation
• The method of sieves and minimum contrast estimators
• Some Limit Theorems for Stationary Processes