Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/18839
DC Field | Value | Language |
---|---|---|
dc.creator | Wu, Berlin | en_US |
dc.creator | 吳柏林 | - |
dc.creator | Chung, Chih-Li | en_US |
dc.date | 2002-01 | en_US |
dc.date.accessioned | 2008-12-24T05:38:51Z | - |
dc.date.available | 2008-12-24T05:38:51Z | - |
dc.date.issued | 2008-12-24T05:38:51Z | - |
dc.identifier.uri | https://nccur.lib.nccu.edu.tw/handle/140.119/18839 | - |
dc.description.abstract | Threshold autoregressive model (TAR model) has certain characteristics due to which linear models fail to fit a nonlinear time series, while the problem of how to find an appropriate threshold value still attracts many researchers’ attention. In this paper, we apply the genetic algorithms to estimate the threshold and lag parameters r and d for TAR models. The selection operator is formulated following Darwin`s principle of survival of the fittest to guide the trek through a search space. The crossover and mutation operators have been inspired by the mechanisms of gene mutation and chromosome recombination. | - |
dc.format | application/ | en_US |
dc.language | en | en_US |
dc.language | en-US | en_US |
dc.language.iso | en_US | - |
dc.relation | Computational Statistics and Data Analysis,38(3),315-330 | en_US |
dc.subject | Genetic algorithms; Threshold autoregressive models; Fitness function; Exchange rate | - |
dc.title | Using Genetic Algorithms to Parameters (d r) Estimation for Threshold Autoregressive Models | en_US |
dc.type | article | en |
dc.identifier.doi | 10.1016/S0167-9473(01)00030-5 | - |
dc.doi.uri | http://dx.doi.org/10.1016/S0167-9473(01)00030-5 | - |
item.languageiso639-1 | en_US | - |
item.fulltext | With Fulltext | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | 期刊論文 |
Files in This Item:
File | Description | Size | Format | |
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315-330.pdf | 176.71 kB | Adobe PDF2 | View/Open |
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