Accuracy of Estimation of Parameters of Linear Regression on Errors in Variables

2010 ◽  
Vol 42 (11) ◽  
pp. 18-30
Author(s):  
Nikolay N. Salnikov
Keyword(s):  
Linear Regression ◽  
Errors In Variables ◽  
Estimation Of Parameters

scholarly journals Evaluation of Impact of the Operational and Technical Factors on Downtime of Municipal Buses Based on a Linear Regression Model

2021 ◽  
Vol 23 (4) ◽  
pp. A241-A247
Author(s):  
Joanna Rymarz ◽  
Anna Borucka ◽  
Andrzej Niewczas
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
External Factors ◽  
Impact Factors ◽  
Estimation Of Parameters ◽  
Failure Type ◽  
Technical Factors ◽  
Failure Types ◽  
Organizational Problems

The objective of this study was to assess the effect of selected operational and technical factors on downtime of vehicles. The sample consisted of buses from a municipal transport company (Poland). Estimation of parameters of a linear regression model was performed. Month of failure (downtime event) and its type were used as predictors. Failures were divided into three categories: events related to the company’s operations, including vehicle failures (1) and other (organizational) problems (2), as well as failures caused by external factors unrelated to the operations of the transport company (3). The downtime was found to be significantly associated with failure type and month of failure. A linear regression model of downtime with a reduced number of impact factors, taking into account two main failure types and two main periods of their occurrence during the year, was developed.

scholarly journals PENERAPAN REGRESI AKAR LATEN DALAM MENANGANI MULTIKOLINEARITAS PADA MODEL REGRESI LINIER BERGANDA

2014 ◽  
Vol 3 (1) ◽  
pp. 8
Author(s):  
DWI LARAS RIYANTINI ◽  
MADE SUSILAWATI ◽  
KARTIKA SARI
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Least Squares Method ◽  
Simulated Data ◽  
Principal Component ◽  
Latent Root ◽  
Estimation Of Parameters ◽  
Independent Variables ◽  
New Variables

Multicollinearity is a problem that often occurs in multiple linear regression. The existence of multicollinearity in the independent variables resulted in a regression model obtained is far from accurate. Latent root regression is an alternative in dealing with the presence of multicollinearity in multiple linear regression. In the latent root regression, multicollinearity was overcome by reducing the original variables into new variables through principal component analysis techniques. In this regression the estimation of parameters is modified least squares method. In this study, the data used are eleven groups of simulated data with varying number of independent variables. Based on the VIF value and the value of correlation, latent root regression is capable of handling multicollinearity completely. On the other hand, a regression model that was obtained by latent root regression has   value of 0.99, which indicates that the independent variables can explain the diversity of the response variables accurately.

scholarly journals Maximum product spacings method for the estimation of parameters of linear regression

2018 ◽  
Vol 1053 ◽  
pp. 012110
Author(s):  
Sukrit Thongkairat ◽  
Woraphon Yamaka ◽  
Songsak Sriboonchitta
Keyword(s):  
Linear Regression ◽  
Estimation Of Parameters

scholarly journals Smoothing splines estimators in functional linear regression with errors-in-variables

2007 ◽  
Vol 51 (10) ◽  
pp. 4832-4848 ◽  
Author(s):  
Hervé Cardot ◽  
Christophe Crambes ◽  
Alois Kneip ◽  
Pascal Sarda
Keyword(s):  
Linear Regression ◽  
Smoothing Splines ◽  
Errors In Variables ◽  
Functional Linear Regression
Sign in / Sign up

Export Citation Format

Share Document