Minimum distance estimation and goodness-of-fit tests in first-order autoregression

DOI10.1214/aos/1176350059zbMath0607.62101OpenAlexW2030836760MaRDI QIDQ1085552

Hira L. Koul

Publication date: 1986

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

Full work available at URL: https://doi.org/10.1214/aos/1176350059

zbMATH Keywords

stationaryleast-squares estimatorergodicminimum distance estimatorinfluence curveasymptotically efficient estimatorCramér-von Mises type statisticfirst- order autoregressive modelgoodness-of-fit tests of symmetryweighted empirical residual process

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items (9)

A fast algorithm for the coordinate-wise minimum distance estimation ⋮ A quadraticity limit theorem useful in linear models ⋮ Minimum distance estimation in linear regression with strong mixing errors ⋮ Semi-parametric estimation of the autoregressive parameter in non-Gaussian Ornstein–Uhlenbeck processes ⋮ Minimum distance estimators for random coefficient autoregressive models ⋮ Computation of certain minimum L₂–distance type estimators under the linear model ⋮ A class of minimum distance estimators in Markovian multiplicative error models ⋮ MinimumL_pNorm Estimator for Simple Linear Regressive Model ⋮ On Koul's minimum distance estimators in the regression models with long memory moving averages.

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