Outliers detection in multivariate spatial linear models |
| |
| Institution: | 1. Institute of Computational Biology, German Research Center for Environmental Health, 85764 Helmholtz Zentrum München, Germany;2. Chair of Mathematical Modeling of Biological Systems, Center for Mathematics, 85748 Garching, Technische Universität München, Germany;3. Department of Mathematics and Statistics, 40014 University of Jyväskylä, Finland;4. Department of Mathematics and Statistics, 20014 University of Turku, Finland;1. School of Statistics, Southwestern University of Finance and Economics, China;2. Department of Mathematical Sciences, Eastern New Mexico University, USA;3. School of Science, Xi’an University of Technology, China;4. Department of Mathematical Sciences, New Mexico State University, USA;1. State Key Laboratory of Multiphase Flow in Power Engineering, Xi''an Jiaotong University, Xi''an 710049, China;2. School of Chemical Engineering and Technology, Xi''an Jiaotong University, Xi''an 710049, China |
| |
| Abstract: | In geostatistics, detecting atypical observations is of special interest due to the changes they can cause in environmental and geological patterns. Several methods for detecting them have been already suggested for the univariate spatial case. However, the problem is more complicated when various variables are observed simultaneously and the spatial correlation among them must be taken into account. The aim of this paper is to detect outliers and influential observations in multivariate spatial linear models. For this purpose, we derive and explore two different methods. First, a multivariate version of the forward search algorithm is given, where locations with outliers are detected in the last steps of the procedure. Next, we derive influence measures to assess the impact of the observations on the multivariate spatial linear model. The procedures are easy to compute and to interpret by means of graphical representations. Finally, an example and a Monte Carlo study illustrate the performance of these methods for identification of outliers in multivariate spatial linear models. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
