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October 3, 2025Open Access

Optimal Estimation for General Gaussian Processes

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Proposed method improves maximum likelihood estimation in Gaussian processes, indicating better statistical inference capabilities.

The exact maximum likelihood estimator shows consistency and asymptotic normality across all parameter estimates.
Joint parameter estimation improves optimal estimation methods by addressing long-standing gaps in Gaussian process analysis.
The method leverages spectral density behavior near zero, enhancing its applicability in diverse statistical contexts.
Simulation results affirm the efficiency of the proposed estimator relative to common methods like the plug-in MLE.

Takabatake et al. (2025) studied this question.

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