Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separable exponential covariance model under fixed domain asymptotics. We first characterize the equivalence of Gaussian measures under this model. Then consistency and asymptotic normality for the maximum l...
- Autores:
-
Velandia Munoz, Daira Luz
Bachoc, François
Bevilacqua, Moreno
Gendre, Xavier
Loubes, Jean Michel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/1878
- Acceso en línea:
- https://hdl.handle.net/11323/1878
https://doi.org/10.1214/17-EJS1298
https://repositorio.cuc.edu.co/
- Palabra clave:
- Bivariate exponential model
Equivalent Gaussian measures
Infill asymptotics
Microergodic parameters
- Rights
- openAccess
- License
- Atribución – No comercial – Compartir igual
| Summary: | We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separable exponential covariance model under fixed domain asymptotics. We first characterize the equivalence of Gaussian measures under this model. Then consistency and asymptotic normality for the maximum likelihood estimator of the microergodic parameters are established. A simulation study is presented in order to compare the finite sample behavior of the maximum likelihood estimator with the given asymptotic distribution. |
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