Sequential change point detection in linear quantile regression models

2015 ◽  
Vol 100 ◽  
pp. 98-103 ◽  
Author(s):  
Mi Zhou ◽  
Huixia Judy Wang ◽  
Yanlin Tang
Keyword(s):  
Quantile Regression ◽  
Change Point ◽  
Regression Models ◽  
Change Point Detection ◽  
Sequential Change ◽  
Point Detection

scholarly journals LPD Communication: A Sequential Change-Point Detection Perspective

2020 ◽  
Vol 68 (4) ◽  
pp. 2474-2490
Author(s):  
Ke-Wen Huang ◽  
Hui-Ming Wang ◽  
Don Towsley ◽  
H. Vincent Poor
Keyword(s):  
Change Point ◽  
Change Point Detection ◽  
Sequential Change ◽  
Point Detection

Self-Normalized Sequential Change-point Detection

2021 ◽  
Author(s):  
Ngai Hang Chan ◽  
Wai Leong Ng ◽  
Chun Yip Yau
Keyword(s):  
Change Point ◽  
Change Point Detection ◽  
Sequential Change ◽  
Point Detection

INTERVAL PIECEWISE REGRESSION MODEL WITH AUTOMATIC CHANGE-POINT DETECTION BY QUADRATIC PROGRAMMING

2005 ◽  
Vol 13 (03) ◽  
pp. 347-361 ◽  
Author(s):  
JING-RUNG YU ◽  
GWO-HSHIUNG TZENG ◽  
HAN-LIN LI
Keyword(s):  
Linear Programming ◽  
Quadratic Programming ◽  
Regression Model ◽  
Change Point ◽  
Regression Models ◽  
Change Point Detection ◽  
Fuzzy Regression ◽  
Programming Approach ◽  
Piecewise Regression ◽  
Point Detection

To handle large variation data, an interval piecewise regression method with automatic change-point detection by quadratic programming is proposed as an alternative to Tanaka and Lee's method. Their unified quadratic programming approach can alleviate the phenomenon where some coefficients tend to become crisp in possibilistic regression by linear programming and also obtain the possibility and necessity models at one time. However, that method can not guarantee the existence of a necessity model if a proper regression model is not assumed especially with large variations in data. Using automatic change-point detection, the proposed method guarantees obtaining the necessity model with better measure of fitness by considering variability in data. Without piecewise terms in estimated model, the proposed method is the same as Tanaka and Lee's model. Therefore, the proposed method is an alternative method to handle data with the large variations, which not only reduces the number of crisp coefficients of the possibility model in linear programming, but also simultaneously obtains the fuzzy regression models, including possibility and necessity models with better fitness. Two examples are presented to demonstrate the proposed method.

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