Parameter estimation for moving averages with positive innovations

DOI10.1214/aoap/1035463327zbMath0881.62093OpenAlexW2026849802MaRDI QIDQ1354836

Marie F. Kratz, Sidney I. Resnick, Paul D. Feigin

Publication date: 12 June 1997

Published in: The Annals of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aoap/1035463327

zbMATH Keywords

linear programmingconsistencyautoregressive processesPoisson processestime series modelsmoving average processes

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05) Point estimation (62F10) Convergence of probability measures (60B10) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (9)

Limit theory for bilinear processes with heavy-tailed noise ⋮ Estimation for heavy tailed moving average process ⋮ Clustering of Markov chain exceedances ⋮ Stable Lévy motion with values in the Skorokhod space: construction and approximation ⋮ On uniform inference in nonlinear models with endogeneity ⋮ Asymptotics of Markov Kernels and the Tail Chain ⋮ Extremes of a class of deterministic sub-sampled processes with applications to stochastic difference equations ⋮ Linear programming-based estimators in simple linear regression ⋮ Estimation for non-negative time series with heavy-tail innovations

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Limit theory for the sample covariance and correlation functions of moving averages
• Extremes of moving averages of random variables from the domain of attraction of the double exponential distribution
• Time series: theory and methods.
• Limit distributions for linear programming time series estimators
• Limit theory for bilinear processes with heavy-tailed noise
• Second-order regular variation and rates of convergence in extreme-value theory
• Estimation for autoregressive processes with positive innovations
• Consistency of Hill's estimator for dependent data
• Testing for independence in heavy tailed and positive innovation time series
• Semidefinite Programming

This page was built for publication: Parameter estimation for moving averages with positive innovations