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Classification of Trends Via the Linear State Space Model

✍ Scribed by C. Gantert


Publisher
John Wiley and Sons
Year
1994
Tongue
English
Weight
666 KB
Volume
36
Category
Article
ISSN
0323-3847

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✦ Synopsis


A method is presented for classification of trend curves based on the linear state space model. In this approach information about the smoothness of the trend curves is incorporated into the classification model by a nonstationary stochastic trend model and can thereby be used to obtain a better classification. In the case of small data sets the performance of the classification is significantly improved in comparison with the usual cluster analysis. Maximum likelihood estimation can be used to calculate the parameters of this model and to determine the classification. The classification algorithm is described in detail and the results are compared to those of the usual cluster analysis by simulation studies and by an application to tree ring data.


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