Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 2
456-459

Method of estimating exogenous variables for measurement error model

Xie Feng1
Cai Ruichu1
Zeng Yan1
Hao Zhifeng1,2
1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
2. School of Mathematics & Big Data, Foshan University, Foshan Guangdong 528000, China
DOI: 10.19734/j.issn.1001-3695.2020.01.0012

Abstract

Causal discovery from the observational dataset has attracted more and more research scholars' attentions in recent years, where the exogenous variable plays an important role in understanding the causal mechanism. However, most existing causal discovery algorithms assume that the observed variables are the real causal variables, and ignore the effects of measurement errors. This paper proposed a method to estimate the exogenous variables under measurement error model. Specifically, by introducing the state-of-the-art triad constraint, the variable that satisfied triad constraint with all the other related paired variables was the exogenous variable. This method could not only solve the dataset with measurement errors, but also handle with the dataset without measurement errors. Applied the algorithm to the data generated by real networks, experimental results show that the proposed method is superior to other existing algorithms regardless of whether the variables contain measurement errors. Meanwhile, the experiment of the real mobile-base-station data also verifies the effectiveness of the algorithm.

Foundation Support

NSFC-广东联合基金资助项目(U1501254)
国家自然科学基金资助项目(61876043)
广东省自然科学基金资助项目(2014A030306004,2014A030308008)
广东特支计划资助项目(2015TQ01X140)
广州市科技计划资助项目(201902010058)
广东省高校优秀青年科研人才国际培养计划资助项目(40190001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.01.0012
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 2
Section: Algorithm Research & Explore
Pages: 456-459
Serial Number: 1001-3695(2021)02-025-0456-04

Publish History

[2021-02-05] Printed Article

Cite This Article

谢峰, 蔡瑞初, 曾艳, 等. 面向测量误差模型的外生变量估计方法 [J]. 计算机应用研究, 2021, 38 (2): 456-459. (Xie Feng, Cai Ruichu, Zeng Yan, et al. Method of estimating exogenous variables for measurement error model [J]. Application Research of Computers, 2021, 38 (2): 456-459. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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