scholarly journals Multiple Structural Change-Point Estimation in Linear Regression Models

2012 ◽  
Vol 19 (3) ◽  
pp. 423-432
Author(s):  
Jae-Hee Kim
Keyword(s):  
Structural Change ◽  
Linear Regression ◽  
Change Point ◽  
Regression Models ◽  
Point Estimation ◽  
Linear Regression Models ◽  
Change Point Estimation

BAYESIAN IDENTIFICATION OF OUTLIERS AND CHANGE-POINTS IN MEASUREMENT ERROR MODELS

2005 ◽  
Vol 08 (04) ◽  
pp. 433-449 ◽  
Author(s):  
FERNANDO A. QUINTANA ◽  
PILAR L. IGLESIAS ◽  
HELENO BOLFARINE
Keyword(s):  
Linear Regression ◽  
Outlier Detection ◽  
Change Point ◽  
Regression Models ◽  
Point Problem ◽  
Linear Regression Models ◽  
Outlier Identification ◽  
Change Point Problem ◽  
Error In Variables ◽  
The Relationship

The problem of outlier and change-point identification has received considerable attention in traditional linear regression models from both, classical and Bayesian standpoints. In contrast, for the case of regression models with measurement errors, also known as error-in-variables models, the corresponding literature is scarce and largely focused on classical solutions for the normal case. The main object of this paper is to propose clustering algorithms for outlier detection and change-point identification in scale mixture of error-in-variables models. We propose an approach based on product partition models (PPMs) which allows one to study clustering for the models under consideration. This includes the change-point problem and outlier detection as special cases. The outlier identification problem is approached by adapting the algorithms developed by Quintana and Iglesias [32] for simple linear regression models. A special algorithm is developed for the change-point problem which can be applied in a more general setup. The methods are illustrated with two applications: (i) outlier identification in a problem involving the relationship between two methods for measuring serum kanamycin in blood samples from babies, and (ii) change-point identification in the relationship between the monthly dollar volume of sales on the Boston Stock Exchange and the combined monthly dollar volumes for the New York and American Stock Exchanges.

scholarly journals Comparison of Simple and Segmented Linear Regression Models on the Effect of Sea Depth toward the Sea Temperature

2021 ◽  
Vol 1 (2) ◽  
pp. 68-75
Author(s):  
Muhammad Bayu Nirwana ◽  
Dewi Wulandari
Keyword(s):  
Linear Regression ◽  
Linear Regression Model ◽  
Change Point ◽  
Regression Models ◽  
Linear Regression Models ◽  
Likelihood Estimator ◽  
Sea Temperature ◽  
Independent Variables ◽  
Modeling Data ◽  
The Relationship

The linear regression model is employed when it is identified a linear relationship between the dependent and independent variables. In some cases, the relationship between the two variables does not generate a linear line, that is, there is a change point at a certain point. Therefore, themaximum likelihood estimator for the linear regression does not produce an accurate model. The objective of this study is to presents the performance of simple linear and segmented linear regression models in which there are breakpoints in the data. The modeling is performed onthe data of depth and sea temperature. The model results display that the segmented linear regression is better in modeling data which contain changing points than the classical one.Received September 1, 2021Revised November 2, 2021Accepted November 11, 2021

scholarly journals Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Fengkai Yang
Keyword(s):  
Linear Regression ◽  
Em Algorithm ◽  
Normal Distribution ◽  
Expectation Maximization ◽  
Change Point ◽  
Estimation Algorithm ◽  
Laplace Distribution ◽  
Point Estimation ◽  
Scale Mixture ◽  
Change Point Estimation

We proposed a robust mean change-point estimation algorithm in linear regression with the assumption that the errors follow the Laplace distribution. By representing the Laplace distribution as an appropriate scale mixture of normal distribution, we developed the expectation maximization (EM) algorithm to estimate the position of mean change-point. We investigated the performance of the algorithm through different simulations, finding that our methods is robust to the distributions of errors and is effective to estimate the position of mean change-point. Finally, we applied our method to the classical Holbert data and detected a change-point.

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
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