Testing for a change point in linear regression models

1998 ◽  
Vol 27 (10) ◽  
pp. 2481-2493 ◽  
Author(s):  
Jie Chen
Keyword(s):  
Linear Regression ◽  
Change Point ◽  
Regression Models ◽  
Linear Regression Models

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 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

scholarly journals The Relationship Between Plasma Tetrahydrocannabinol Levels and Intraocular Pressure in Healthy Adult Subjects

2022 ◽  
Vol 8 ◽  
Author(s):  
Sameh Mosaed ◽  
Andrew K. Smith ◽  
John H. K. Liu ◽  
Donald S. Minckler ◽  
Robert L. Fitzgerald ◽  
...  
Keyword(s):  
Intraocular Pressure ◽  
Linear Regression ◽  
Change Point ◽  
Plasma Levels ◽  
Regression Models ◽  
Healthy Adult ◽  
Correlation Coefficients ◽  
Linear Regression Models ◽  
Percentage Decrease ◽  
The Relationship

BackgroundΔ9-tetrahydrocannabinol (THC) has been shown to decreased intraocular pressure (IOP). This project aims to define the relationship between plasma THC levels and IOP in healthy adult subjects.MethodsEleven healthy subjects received a single dose of inhaled cannabis that was self-administered in negative pressure rooms. Measurements of IOP and plasma THC levels were taken at baseline and every 30 min for 1 h and afterwards every hour for 4 h. IOP reduction and percent change in IOP over time were calculated. Linear regression models were used to measure the relationship between IOP and plasma THC levels. Two line linear regression models with F-tests were used to detect change points in the regression. Then, Pearson correlations were computed based on the change point.ResultsTwenty-two eyes met inclusion criteria. The average peak percentage decrease in IOP was 16% at 60 min. Percent IOP reduction as well as total IOP reduction demonstrated a negative correlation with THC plasma levels showing r-values of −0.81 and −0.70, respectively. F-tests revealed a change point in the regression for plasma levels >20 ng/ml. For levels >20 ng/ml, the correlation coefficients changed significantly with r-values of 0.21 and 0.29 (p < 0.01).ConclusionPlasma THC levels are significantly correlated with IOP reduction up to plasma levels of 20 ng/ml. Plasma levels >20 ng/ml were not correlated with further decrease in IOP. More research is needed to determine the efficacy of THC in reducing IOP for eyes with ocular hypertension and glaucoma.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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