| 期刊全称 | Bilinear Stochastic Models and Related Problems of Nonlinear Time Series Analysis | | 期刊简称 | A Frequency Domain A | | 影响因子2023 | György Terdik | | 视频video | | | 学科分类 | Lecture Notes in Statistics | | 图书封面 |  | | 影响因子 | "Ninety percent of inspiration is perspiration. " [31] The Wiener approach to nonlinear stochastic systems [146] permits the representation of single-valued systems with memory for which a small per turbation of the input produces a small perturbation of the output. The Wiener functional series representation contains many transfer functions to describe entirely the input-output connections. Although, theoretically, these representations are elegant, in practice it is not feasible to estimate all the finite-order transfer functions (or the kernels) from a finite sam ple. One of the most important classes of stochastic systems, especially from a statistical point of view, is the case when all the transfer functions are determined by finitely many parameters. Therefore, one has to seek a finite-parameter nonlinear model which can adequately represent non linearity in a series. Among the special classes of nonlinear models that have been studied are the bilinear processes, which have found applica tions both in econometrics and control theory; see, for example, Granger and Andersen [43] and Ruberti, et al. [4]. These bilinear processes are de fined to be linear in both input and | | Pindex | Book 1999 |
The information of publication is updating
|
|
发表于 2025-3-21 18:13:45
