Parameter estimation in linear regression driven by a Gaussian sheet
The problem of estimating the parameters of a linear regression model Z(s,t) = migi(s, £) + •••+ mpgp(s, t) + U(s, t) based on observations of Z on a spatial domain G of special shape is considered, where the driving process U is a Gaussian random field and pi,... , gp are known functions. Explicit...
Elmentve itt :
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| Dokumentumtípus: | Cikk |
| Megjelent: |
Bolyai Institute, University of Szeged
Szeged
2012
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| Sorozat: | Acta scientiarum mathematicarum
78 No. 3-4 |
| Kulcsszavak: | Matematika |
| Tárgyszavak: | |
| Online Access: | http://acta.bibl.u-szeged.hu/16456 |
| Tartalmi kivonat: | The problem of estimating the parameters of a linear regression model Z(s,t) = migi(s, £) + •••+ mpgp(s, t) + U(s, t) based on observations of Z on a spatial domain G of special shape is considered, where the driving process U is a Gaussian random field and pi,... , gp are known functions. Explicit forms of the maximum-likelihood estimators of the parameters are derived in the cases when U is either a Wiener or a stationary or nonstationary Ornstein-Uhlenbeck sheet. Simulation results are also presented, where the driving random sheets are simulated with the help of their Karhunen-Loève expansions. |
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| Terjedelem/Fizikai jellemzők: | 689-713 |
| ISSN: | 0001-6969 |