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Autre Publication Scientifique Année : 2022

Fitting a Powered Exponential Autocorrelation: Alternatives to Maximum Likelihood via Conjugate Gradient Linear Solvers

Didier A. Girard

Résumé

https://demonstrations.wolfram.com/FittingAPoweredExponentialAutocorrelationAlternativesToMaxim/. Fitting a Powered Exponential Autocorrelation: Alternatives to Maximum Likelihood via Conjugate Gradient Linear Solvers. The setting and the method are the same as in the Demonstration "Estimating a Centered Matérn (1) Process: Three Alternatives to Maximum Likelihood via Conjugate Gradient Linear Solvers", except that the autocorrelation function, which was the Matérn function with differentiability-parameter $\nu$ equal to 1, is now replaced by the powered exponential autocorrelation, with various possible values for the power $p$. Note that $p$ is assumed known. An important difference in the implementation of the fixed-point algorithm (for approximating the root of the GE-EV estimating equation) is that the algorithm must be appropriately modified as a function of $p$. It is observed here that this implementation is quite fast, even for a data size as large as 8196.
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Dates et versions

hal-03701435 , version 1 (22-06-2022)

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Paternité - Pas d'utilisation commerciale - Partage selon les Conditions Initiales

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  • HAL Id : hal-03701435 , version 1

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Didier A. Girard. Fitting a Powered Exponential Autocorrelation: Alternatives to Maximum Likelihood via Conjugate Gradient Linear Solvers. 2022. ⟨hal-03701435⟩
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