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Bayes Procedures for the Classification of Multiple Polynomial Trends with Dependent Residuals

 

作者: BenL. Browdy,   PotterC. Chang,  

 

期刊: Journal of the American Statistical Association  （Taylor Available online 1982）
卷期: Volume 77, issue 378  

页码: 483-487

 

ISSN:0162-1459

 

年代: 1982

 

DOI:10.1080/01621459.1982.10477836

 

出版商: Taylor & Francis Group

 

关键词: Classification;Trend;Linear discriminant function;Polynomial regression function;Autoregressive-moving average process;Bayes procedure

 

数据来源: Taylor

 

摘要:

Three procedures for the classification of multivariate time-dependent data are examined. Two are shown to be Bayes procedures assuming that the time dependence of the data can be described by polynomial regression functions and that the relation among successive residuals is described by a stationary and invertible ARMA process. Relative advantages of these procedures in practical applications and limitations of the model are discussed.

 

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