Publication: Asymptotic analysis of ML-covariance parameter estimators based on covariance approximations
Asymptotic analysis of ML-covariance parameter estimators based on covariance approximations
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Furrer, R., & Hediger, M. (2023). Asymptotic analysis of ML-covariance parameter estimators based on covariance approximations. Electronic Journal of Statistics, 17, 3050–3102. https://doi.org/10.1214/23-ejs2170
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Given a zero-mean Gaussian random field with a covariance function that belongs to a parametric family of covariance functions, we introduce a new notion of likelihood approximations, termed truncatedlikelihood functions. Truncated-likelihood functions are based on direct functional approximations of the presumed family of covariance functions. For compactly supported covariance functions, within an increasing-domain asymptotic framework, we provide sufficient conditions under which consistency and asymptotic normality of estimators b
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- Hediger, Michael
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Furrer, R., & Hediger, M. (2023). Asymptotic analysis of ML-covariance parameter estimators based on covariance approximations. Electronic Journal of Statistics, 17, 3050–3102. https://doi.org/10.1214/23-ejs2170
