Regularized Autoregressive Approximation in Time Series
In applications, the true underlying model of an observed time series is typically unknown or has a complicated structure. A common approach is to approximate the true model by autoregressive (AR) equation whose orders are chosen by information criterions such as AIC, BIC and Parsen's CAT and w...
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| Language: | en |
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2008
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| Online Access: | http://hdl.handle.net/10012/3766 |