Estimation theory in Hilbert spaces and its applications. (English) Zbl 0564.62079
Stochastic analysis and applications, Adv. Probab. Relat. Top. 7, 367-410 (1984).
[For the entire collection see Zbl 0541.00008.]
This chapter follows a unified Wiener-Hopf approach in deriving optimal filtering and smoothing algorithms for discrete-time distributed- parameter systems. The results are applied to the estimation of atmospheric sulfur dioxide concentrations from Japanese air-pollution data. The one-hour ahead prediction errors were quite comparable to those from a minimum-AIC autoregressive model fitted from the same data.
This chapter follows a unified Wiener-Hopf approach in deriving optimal filtering and smoothing algorithms for discrete-time distributed- parameter systems. The results are applied to the estimation of atmospheric sulfur dioxide concentrations from Japanese air-pollution data. The one-hour ahead prediction errors were quite comparable to those from a minimum-AIC autoregressive model fitted from the same data.
Reviewer: E.Slud
MSC:
| 62M20 | Inference from stochastic processes and prediction |
| 60G35 | Signal detection and filtering (aspects of stochastic processes) |
| 93E11 | Filtering in stochastic control theory |
| 60G25 | Prediction theory (aspects of stochastic processes) |
| 93E10 | Estimation and detection in stochastic control theory |
| 93E14 | Data smoothing in stochastic control theory |
| 62N99 | Survival analysis and censored data |
| 62P99 | Applications of statistics |
