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Composite likelihood inference of fractional Gaussian pro...
[Submitted on 10 Jun 2026 (v1), last revised 14 Jul 2026 (this v · 2026-06-11 · via stat updates on arXiv.org

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Abstract:The composite likelihood method reduces the computational cost of parameter estimation in time series by considering several subsets of observations instead of all observations at once. The asymptotic properties of this method are related to the Godambe information, an extension of the Fisher information that accounts for the dependence between subsets of observations. We aim to apply this method to linear Gaussian models, in particular fractional Brownian motion and fractional Gaussian noise. We derive theoretical expressions for their Fisher information and their Godambe information and deduce a subset selection design that sequentially maximizes the Godambe information. The size of the subsets then allows us to control the trade-off between estimation accuracy and computational cost. Through simulations, we compare this method with the method of moments and maximum likelihood estimation, and we apply it to real data, namely volatility series of a stock index and a wind speed time series.

Submission history

From: Matthieu Garcin [view email]
[v1] Wed, 10 Jun 2026 11:41:54 UTC (225 KB)
[v2] Tue, 14 Jul 2026 12:36:16 UTC (226 KB)