On the Whittle estimator for linear random noise spectral density parameter in continuous-time nonlinear regression models

DOI10.1007/s11203-019-09206-zzbMath1436.62429arXiv1909.10457OpenAlexW2973322050MaRDI QIDQ1984649

Alexander V. Ivanov, Igor V. Orlovskyi, Nikolai N. Leonenko

Publication date: 7 April 2020

Published in: Statistical Inference for Stochastic Processes (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.10457

zbMATH Keywords

consistencyasymptotic normalityspectral densityleast squares estimatorWhittle estimatornonlinear regression modelLevitan polynomialsLévy-driven linear noise process

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inference from stochastic processes and spectral analysis (62M15) General nonlinear regression (62J02) Nonlinear processes (e.g., (G)-Brownian motion, (G)-Lévy processes) (60G65)

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

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