Circular data as any other types of data are subjected to contamination with some unexpected observations which are known outliers. In this paper, four tests of discordancy for circular data based on M, C, D, and A st...Circular data as any other types of data are subjected to contamination with some unexpected observations which are known outliers. In this paper, four tests of discordancy for circular data based on M, C, D, and A statistics are extended to the wrapped Cauchy distribution to detect possible outliers. The cut-off points and the power of performances are investigated via extensive simulation study. Results show that tests perform better as the concentration of the samples is increased. Two real circular data sets are analysed for illustration.展开更多
提出了一种基于双帧动态时间规整(Double Frame Dynamic Time Wrapping)的识别方法,通过缩小帧匹配距离矩阵(Frame Match Distance Matrix)的规模从而减少计算量,提高识别速度.实验表明,基于双帧动态时间规整的识别方法在识别率有所提...提出了一种基于双帧动态时间规整(Double Frame Dynamic Time Wrapping)的识别方法,通过缩小帧匹配距离矩阵(Frame Match Distance Matrix)的规模从而减少计算量,提高识别速度.实验表明,基于双帧动态时间规整的识别方法在识别率有所提高的条件下,缩短了识别时间,有很好的实用价值.展开更多
文摘Circular data as any other types of data are subjected to contamination with some unexpected observations which are known outliers. In this paper, four tests of discordancy for circular data based on M, C, D, and A statistics are extended to the wrapped Cauchy distribution to detect possible outliers. The cut-off points and the power of performances are investigated via extensive simulation study. Results show that tests perform better as the concentration of the samples is increased. Two real circular data sets are analysed for illustration.
文摘提出了一种基于双帧动态时间规整(Double Frame Dynamic Time Wrapping)的识别方法,通过缩小帧匹配距离矩阵(Frame Match Distance Matrix)的规模从而减少计算量,提高识别速度.实验表明,基于双帧动态时间规整的识别方法在识别率有所提高的条件下,缩短了识别时间,有很好的实用价值.