GENERAL LINEAR PROCESSES:A PROPERTY OF THE EMPIRICAL PROCESS APPLIED TO DENSITY AND MODE ESTIMATION

DOI10.1111/j.1467-9892.1990.tb00051.xzbMath0719.62049OpenAlexW2082129923MaRDI QIDQ5751767

Kamal C. Chanda, Frits H. Ruymgaart

Publication date: 1990

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.1990.tb00051.x

zbMATH Keywords

mode estimationempirical processdensity estimationstationary linear processm-dependent random vectorsmaximal inequalities for local fluctuations

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric estimation (62G05) Stationary stochastic processes (60G10)

Related Items (12)

Conditional empirical, quantile and difference processes for a large class of time series with applications ⋮ Asymptotic behavior of \(L\)-statistics for a large class of time series ⋮ Limit theorems for functionals of moving averages ⋮ On histograms for linear processes ⋮ Some stochastic inequalities and asymptotic normality of serial rank statistics in general linear processes ⋮ Curve estimation for \(m_ n\)-decomposable time series including bilinear processes ⋮ A triangular central limit theorem under a new weak dependence condition ⋮ Some asymptotic results for a broad class of nonparametric statistics ⋮ Empirical U-statistics processes ⋮ Asymptotic normality of \(L\)-statistics based on \(m(n)\)-decomposable time series ⋮ On the asymptotic expansion of the empirical process of long-memory moving averages ⋮ Nonparametric prediction for random fields

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Density estimation for linear processes
• Some stochastic inequalities and asymptotic normality of serial rank statistics in general linear processes
• Extremes and related properties of random sequences and processes
• Some mixing properties of time series models
• Sampling distribution for a class of estimators for nonregular linear processes
• A note on strong mixing of ARMA processes
• Weak convergence of generalized empirical processes relative to \(d_q\) under strong mixing
• Non-strong mixing autoregressive processes
• Linear processes and bispectra
• On the Strong Mixing Property for Linear Sequences
• Strong mixing properties of linear stochastic processes
• On Estimation of a Probability Density Function and Mode

This page was built for publication: GENERAL LINEAR PROCESSES:A PROPERTY OF THE EMPIRICAL PROCESS APPLIED TO DENSITY AND MODE ESTIMATION