Asymptotic normality of the correlogram estimator of the covariance function of a random noise in the nonlinear regression model

DOI10.1090/tpms/966zbMath1346.60018OpenAlexW2470679744MaRDI QIDQ2786943

K. K. Moskvichova, Alexander V. Ivanov

Publication date: 24 February 2016

Published in: Theory of Probability and Mathematical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1090/tpms/966

zbMATH Keywords

asymptotic normalitycovariance functionconvergence in distributionstationary Gaussian processspectral densityrandom elementnonlinear regression modelcorrelogram estimator

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Gaussian processes (60G15) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05) Stationary stochastic processes (60G10) Sums of independent random variables; random walks (60G50) Functional limit theorems; invariance principles (60F17)

Related Items (2)

Large deviations of the correlogram estimator of the random noise covariance function in the nonlinear regression model ⋮ Asymptotic normality of the residual correlogram in the continuous-time nonlinear regression model

Cites Work

• Asymptotic expansion of the moments of correlogram estimator for the random-noise covariance function in the nonlinear regression model
• Stochastic asymptotic expansion of correlogram estimator of the correlation function of random noise in nonlinear regression model
• On the properties of an empirical correlogram of a Gaussian process with square integrable spectral density
• Estimation of harmonic component in regression with cyclically dependent errors
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