Detection of outliers in mixed regressive-spatial autoregressive models

被引：13

作者：

Jin, Libin ^{[1
]}

Dai, Xiaowen ^{[1
]}

Shi, Anqi ^{[2
]}

Shi, Lei ^{[3
,4
]}

机构：

[1] Renmin Univ China, Sch Stat, Beijing, Peoples R China

[2] Univ Wisconsin, Coll Letters & Sci, Madison, WI USA

[3] Yunnan Univ Finance & Econ, Sch Math & Stat, Kunming 650221, Peoples R China

[4] Yunnan TongChuang Sci Comp & Data Min Ctr, Kunming, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2016年 / 45卷 / 17期

基金：

中国国家自然科学基金;

关键词：

Mean-shift model; Mixed regressive-spatial autoregressive model; Outliers; Score test; Variance-weight model;

D O I：

10.1080/03610926.2014.941493

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This article studies the outlier detection problem in mixed regressive-spatial autoregressive model. The formulae for testing outliers and their approximate distributions are derived under the mean-shift model and the variance-weight model, respectively. The simulation studies are conducted for examining the power and size of the test, as well as for the detection of outliers when a simulated data contains several outliers. A real data is analyzed to illustrate the proposed method, and modified models based on mean-shift and variance-weight models in which detected outliers are taken into account are suggested to deal with the outliers and confirm theconclusions.

引用

页码：5179 / 5192

页数：14

共 16 条

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[2]

Barnett V., 1994, Outliers in statistical data

[3]

Bruce A. G., 1989, J R STAT SOC B, V73, P533

[4]

Cook RD., 1982, Residuals and influence in regression