| 线性回归模型中方差变点的连续监测 |
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| 引用本文： | 陈占寿,田铮,秦瑞兵,冷成财. 线性回归模型中方差变点的连续监测[J]. 数学研究及应用, 2010, 30(4): 610-618. DOI: 10.3770/j.issn:1000-341X.2010.04.005 |
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| 作者姓名： | 陈占寿 田铮 秦瑞兵 冷成财 |
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| 作者单位： | 西北工业大学应用数学系，陕西 西安 710129;西北工业大学应用数学系，陕西 西安 710129；中国科学院自动化研究所模式识别国家重点实验室, 北京 100080;西北工业大学应用数学系，陕西 西安 710129;西北工业大学应用数学系，陕西 西安 710129 |
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| 基金项目： | 国家自然科学基金(Grant Nos.60972150, 10926197),西北工业大学科技创新基金(Grant No.2007KJ01033). |
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| 摘 要： | The paper investigates the sequential observations’ variance change in linear regression model. The procedure is based on a detection function constructed by residual squares of CUSUM and a boundary function which is designed so that the test has a small probability of false alarm and asymptotic power one. Simulation results show our monitoring procedure performs well when variance change occurs shortly after the monitoring time. The method is still feasible for regression coefficients change or both variance and regression coefficients change problem.
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| 关 键 词： | sequential monitoring variance change linear regression model residuals. |
| 收稿时间： | 2008-10-09 |
| 修稿时间： | 2009-09-15 |
Sequential Monitoring Variance Change in Linear Regression Model |
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| Zhan Shou CHEN,Zheng TIAN,Rui Bing QIN and Cheng Cai LENG. Sequential Monitoring Variance Change in Linear Regression Model[J]. Journal of Mathematical Research with Applications, 2010, 30(4): 610-618. DOI: 10.3770/j.issn:1000-341X.2010.04.005 |
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| Authors: | Zhan Shou CHEN Zheng TIAN Rui Bing QIN Cheng Cai LENG |
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| Affiliation: | 1. Department of Applied Mathematics, Northwestern Polytechnical University, Shaanxi 710129, P. R. China
2. Department of Applied Mathematics, Northwestern Polytechnical University, Shaanxi 710129, P. R. China;State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, P. R. China |
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| Abstract: | The paper investigates the sequential observations' variance change in linear regression model. The procedure is based on a detection function constructed by residual squares of CUSUM and a boundary function which is designed so that the test has a small probability of false alarm and asymptotic power one. Simulation results show our monitoring procedure performs well when variance change occurs shortly after the monitoring time. The method is still feasible for regression coefficients change or both variance and regression coefficients change problem. |
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| Keywords: | sequential monitoring variance change linear regression model residuals. |
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