Parameter estimation and hypothesis testing in stationary vector time series

DOI10.1016/S0167-7152(96)00131-9zbMath0899.62117MaRDI QIDQ1380591

Yoshihide Kakizawa

Publication date: 8 March 1998

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

zbMATH Keywords

stationary processesspectral density matricesdisparity measure

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Inference from stochastic processes and spectral analysis (62M15) Asymptotic properties of parametric tests (62F05)

Related Items (1)

Estimation for non-Gaussian locally stationary processes with empirical likelihood method

Cites Work

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• On the estimation of the parameters of a power spectrum
• Weighted least squares estimators on the frequency domain for the parameters of a time series
• A central limit theorem for stationary processes and the parameter estimation of linear processes
• Alternative models for stationary stochastic processes
• A central limit theorem for parameter estimation in stationary vector time series and its application to models for a signal observed with noise
• On the applications of divergence type measures in testing statistical hypotheses
• Automatic Frequency Domain Inference on Semiparametric and Nonparametric Models

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