AN EMPIRICAL LIKELIHOOD APPROACH FOR NON‐GAUSSIAN VECTOR STATIONARY PROCESSES AND ITS APPLICATION TO MINIMUM CONTRAST ESTIMATION

DOI10.1111/J.1467-842X.2010.00585.XzbMath1373.62452OpenAlexW2110866212MaRDI QIDQ5357573

Hiroaki Ogata, Masanobu Taniguchi

Publication date: 11 September 2017

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-842x.2010.00585.x

zbMATH Keywords

empirical likelihoodestimating functionWhittle likelihoodminimum contrast estimationspectral density matrix

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05) Inference from stochastic processes and spectral analysis (62M15)

Related Items (8)

On copula moment: empirical likelihood based estimation method ⋮ An empirical likelihood approach for symmetric \(\alpha\)-stable processes ⋮ Self-weighted generalized empirical likelihood methods for hypothesis testing in infinite variance ARMA models ⋮ A review of empirical likelihood methods for time series ⋮ Empirical likelihood approach toward discriminant analysis for dynamics of stable processes ⋮ Frequency domain generalized empirical likelihood method ⋮ Bartlett Correction of Empirical Likelihood for Non‐Gaussian Short‐Memory Time Series ⋮ Robust causality test of infinite variance processes

This page was built for publication: AN EMPIRICAL LIKELIHOOD APPROACH FOR NON‐GAUSSIAN VECTOR STATIONARY PROCESSES AND ITS APPLICATION TO MINIMUM CONTRAST ESTIMATION