Presentation information

Organized Session

Organized Session » OS-7

[2C4-OS-7a] OS-7 (1)

Wed. Jun 10, 2020 1:50 PM - 3:30 PM Room C (jsai2020online-3)

藤井 慶輔(名古屋大学)、竹内 孝(NTT)、竹内 一郎(名古屋工業大学)、田部井 靖生(理化学研究所)、依田 憲(名古屋大学)、前川 卓也(大阪大学)

2:50 PM - 3:10 PM

[2C4-OS-7a-04] Trajectory Segmentation with Statistical Reliability Guarantee

〇Hiroki Toda1, Duy Nguyen Le Vo1, Ryota Sugiyama1, Takuto Sakuma1, Yuichi Mizutani2, Hirokazu Suzuki2, Ken Yoda2, Ichiro Takeuchi1 (1. Nagoya Institute of Technology, 2. Nagoya University)

Keywords:trajectory data analysis, time series segmentation, change point detection, hypothesis testing, selective inference

The goal of this study is to develop a method for evaluating the statistical significance of trajectory segmentation results. The difficulty of this problem lies in the fact that the trajectory segments are identified by a segmentation algorithm, and this fact must be properly incorporated in the statistical inference. Unfortunately, if one uses traditional statistical inference, the $p$-values or confidence intervals are not valid anymore in the sense that the false positive rate cannot be controlled at the desired significance level anymore. To resolve this difficulty, we adopt Selective Inference (SI) framework. We propose a new SI method for trajectory segmentation results obtained by dynamic programming --- a common method for optimal segmentation --- which provides valid $p$-values or confidence intervals. We applied the proposed method to animal trajectory data and demonstrate the difference between the traditional invalid method and the proposed valid method.

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