In this paper we propose a new approach for estimating the unknown parameter in the stochastic linear regressive model with stationary ergodic sequence of covariates. Under mild conditions on the joint distribution of the covariate and the error, the estimator constructed is shown to be strongly con
A general procedure for stochastic modelling of systems consisting of a large number of interacting components
β Scribed by Hesse, Christian H.
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 119 KB
- Volume
- 13
- Category
- Article
- ISSN
- 8755-0024
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β¦ Synopsis
Multi-component systems with interaction are widespread in all the natural sciences and beyond. In this paper, we discuss a general two-stage procedure for modelling these systems. The procedure is based on first identifying the main influences on incremental system evolution in some phase space and capturing these in a parametric stochastic process structure. The remaining influences are then used for locally fine-tuning the parameters of these processes. The procedure is discussed in the context of particle sedimentation in viscous fluids and is also applied to the study of interregional migration in population demography. Other applications are indicated.
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