Limit distributions for linear programming time series estimators

DOI10.1016/0304-4149(94)90022-1zbMath0819.62070OpenAlexW2165884617MaRDI QIDQ1332320

Paul D. Feigin, Sidney I. Resnick

Publication date: 10 October 1994

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://hdl.handle.net/1813/8918

zbMATH Keywords

rate of convergencelinear programmingtime seriesregular variationYule-Walker estimatorPoisson processeslimit distributiontailconsistent parameter estimatorspositive innovationsstationary autoregressive processes

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Stationary stochastic processes (60G10) Applications of mathematical programming (90C90)

Related Items (14)

Testing the tail index in autoregressive models ⋮ Parameter estimation for moving averages with positive innovations ⋮ Limit theory for bilinear processes with heavy-tailed noise ⋮ Extremal quantile regression ⋮ Test for tail index change in stationary time series with Pareto-type marginal distribution ⋮ A class of bootstrap tests on the tail index ⋮ Extremal quantile treatment effects ⋮ A note on maximum autoregressive processes of order one ⋮ Linear programming-based estimators in simple linear regression ⋮ Estimation for a first-order bifurcating autoregressive process with heavy-tail innovations ⋮ Estimation for a class of positive nonlinear time series models ⋮ Estimation for non-negative time series with heavy-tail innovations ⋮ Least tail-trimmed squares for infinite variance autoregressions ⋮ Estimation of the index parameter for autoregressive data using the estimated innovations

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