Multiple Change-Point Detection in Piecewise Exponential Hazard Regression Models with Long-Term Survivors and Right Censoring

2013 ◽  
pp. 289-304 ◽  
Author(s):  
Lianfen Qian ◽  
Wei Zhang
Keyword(s):  
Change Point ◽  
Regression Models ◽  
Right Censoring ◽  
Change Point Detection ◽  
Hazard Regression ◽  
Piecewise Exponential ◽  
Point Detection ◽  
Long Term Survivors

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.

scholarly journals Online Change-Point Detection of Linear Regression Models

2019 ◽  
Vol 67 (12) ◽  
pp. 3316-3329
Author(s):  
Jun Geng ◽  
Bingwen Zhang ◽  
Lauren M. Huie ◽  
Lifeng Lai
Keyword(s):  
Linear Regression ◽  
Change Point ◽  
Regression Models ◽  
Change Point Detection ◽  
Linear Regression Models ◽  
Point Detection

Robust change point detection for linear regression models

2019 ◽  
Vol 12 (2) ◽  
pp. 203-213
Author(s):  
Aylin Alin ◽  
Ufuk Beyaztas ◽  
Michael A. Martin
Keyword(s):  
Linear Regression ◽  
Change Point ◽  
Regression Models ◽  
Change Point Detection ◽  
Linear Regression Models ◽  
Point Detection

Estimation in a change-point hazard regression model with long-term survivors

2013 ◽  
Vol 83 (7) ◽  
pp. 1683-1691 ◽  
Author(s):  
Yunxia Li ◽  
Lianfen Qian ◽  
Wei Zhang
Keyword(s):  
Regression Model ◽  
Change Point ◽  
Hazard Regression ◽  
Long Term Survivors

scholarly journals Change Point Detection of Sustainable Periods of Economic Systems Under the Robust Control

2019 ◽  
Vol 26 (2) ◽  
pp. 27-36
Author(s):  
S. E. Khrushchev ◽  
M. A. Alekseev ◽  
O. M. Logachova
Keyword(s):  
Robust Control ◽  
Change Point ◽  
Regression Models ◽  
Small Volume ◽  
Statistical Modelling ◽  
Change Point Detection ◽  
Economic Systems ◽  
Non Gaussian ◽  
Point Detection ◽  
And Control

This article addresses the potential of mathematical and statistical modelling the change point detection in economic systems on the example of UC «RUSAL». Change point prediction of stable or quasi-stable periods of economic systems is necessary for the operational changing of a strategy, tactics and control of the considered economic system. It solves one of the robust control problems, the purpose of which is the synthesis of the regulator that can provide the preservation of output variables of the system within the robust limit for all types of membership functions and the uncertainty of the input data.The developed algorithm is based on the study of the behavior of residuals of regression models by the observed series of the dynamics of some exponent (as a benchmark was chosen the price of ordinary share). This algorithm is applicable for small volume samples, which, as a rule, are the series of dynamics of exponents of economic systems and also, in the study of non-Gaussian observational models.

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