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 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.

Sampling Experiments on the Estimation of Parameters in Heteroscedastic Linear Regression

Biometrics ◽  
1973 ◽  
Vol 29 (4) ◽  
pp. 771 ◽  
Author(s):  
John A. Jacquez ◽  
Marija Norusis
Keyword(s):  
Linear Regression ◽  
Estimation Of Parameters

scholarly journals A Treatise on Ordinary Least Squares Estimation of Parameters of Linear Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 518
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
C. Narayana ◽  
J. Ravi sankar ◽  
P. Balasiddamuni
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Linear Models ◽  
Ordinary Least Squares ◽  
Linear Estimation ◽  
Estimation Of Parameters ◽  
Parametric Function ◽  
Ols Regression

This research article primarily focuses on the estimation of parameters of a linear regression model by the method of ordinary least squares and depicts Gauss-Mark off theorem for linear estimation which is useful to find the BLUE of a linear parametric function of the classical linear regression model. A proof of generalized Gauss-Mark off theorem for linear estimation has been presented in this memoir.  Ordinary Least Squares (OLS) regression is one of the major techniques applied to analyse data and forms the basics of many other techniques, e.g. ANOVA and generalized linear models [1]. The use of this method can be extended with the use of dummy variable coding to include grouped explanatory variables [2] and data transformation models [3]. OLS regression is particularly powerful as it relatively easy to check the model assumption such as linearity, constant, variance and the effect of outliers using simple graphical methods [4]. J.T. Kilmer et.al [5] applied OLS method to evolutionary and studies of algometry.  

scholarly journals Market Openness and Its Relationship to Connecting Markets Due to COVID-19

2021 ◽  
Vol 13 (19) ◽  
pp. 10964
Author(s):  
Karime Chahuán-Jiménez ◽  
Rolando Rubilar-Torrealba ◽  
Hanns de la Fuente-Mella
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mixed Model ◽  
Financial Contagion ◽  
Estimation Of Parameters ◽  
Health Crisis ◽  
Economic Markets ◽  
Trade Relationships ◽  
Mixed Linear Regression Model

In this research, statistical models were formulated to study the effect of the health crisis arising from COVID-19 in economic markets. Economic markets experience economic crises irrespective of effects corresponding to financial contagion. This investigation was based on a mixed linear regression model that contains both fixed and random effects for the estimation of parameters and a mixed linear regression model corresponding to the generalisation of a linear model using the incorporation of random deviations and used data on the evolution of the international trade of a group of 42 countries, in order to quantify the effect that COVID-19 has had on their trade relationships and considering the average state of trade relationships before the global pandemic was declared and its subsequent effects. To measure, quantify and model the effect of COVID-19 on trade relationships, three main indicators were used: imports, exports and the sum of imports and exports, using six model specifications for the variation in foreign trade as response variables. The results suggest that trade openness, measured through the trade variable, should be modelled with a mixed model, while imports and exports can be modelled with an ordinary linear regression model. The trade relationship between countries with greater economic openness (using imports and exports as a trade variable) has a higher correlation with the country’s health index and its effect on the financial market through its main trading index; the same is true for country risk. However, regarding the association with OECD membership, the relations are only with imports.

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