Sensitivity analysis and visualization for functional data

I. Chung Hsieh, Yufen Huang

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


When analyzing functional data processes, the presence of outliers can greatly influence modelling and forecasting outcomes and lead to the inaccurate conclusion. Hence, detection of such outliers becomes an essential task. Visualization of data not only plays a vital role in discovering the features of data before applying statistical models and summary statistics but also provides an auxiliary tool in identifying outliers. The research involving visualization and sensitivity analysis for functional data has not yet received much attention in the literature to date. Thus, this becomes the focus of this paper. To this end, we propose a method combining influence function with iteration scheme for identifying outliers and develop new visualization tools for displaying features and grasping the outliers in functional data. Furthermore, comparisons between our proposed methods with the existing methods are also investigated. Finally, we illustrate these proposed methods with simulation studies and real data examples.

頁(從 - 到)1593-1615
期刊Journal of Statistical Computation and Simulation
出版狀態Published - 2021

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 建模與模擬
  • 統計、概率和不確定性
  • 應用數學


深入研究「Sensitivity analysis and visualization for functional data」主題。共同形成了獨特的指紋。