Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms

A regression model was tested to examine the relation between uncertainty about choice of study and regulation activities in three groups of freshmen ( N = 566). Invariance of regression coefficients, intercept, and residual error terms over groups was found. The strongest explanatory variable of uncertainty about choice of study was lack of regulation.

Download Full-text

Robustness of Some Estimators of Linear Model with Autocorrelated Error Terms When Stochastic Regressors are Normally Distributed

Journal of Modern Applied Statistical Methods ◽

10.22237/jmasm/1209615540 ◽

2008 ◽

Vol 7 (1) ◽

pp. 246-252 ◽

Cited By ~ 2

Author(s):

Kayode Ayinde ◽

J. O. Olaomi

Keyword(s):

Linear Model ◽

Autocorrelated Error ◽

Error Terms ◽

Normally Distributed

Download Full-text

Performances Of Estimators Of Linear Models With Autocorrelated Error Terms When The Independent Variable Is Autoregressive

Global Journal of Pure and Applied Sciences ◽

10.4314/gjpas.v11i1.16472 ◽

2005 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

JC Nwabueze

Keyword(s):

Linear Models ◽

Autocorrelated Error ◽

Independent Variable ◽

Error Terms

Download Full-text

Estimation of the parameters of linear regression model with autocorrelated error terms which are also correlated with the regressor

Global Journal of Pure and Applied Sciences ◽

10.4314/gjpas.v13i2.16700 ◽

2007 ◽

Vol 13 (2) ◽

Author(s):

JO Olaomi

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Autocorrelated Error ◽

Error Terms

Download Full-text

On the Performances of Classical VAR and Sims-Zha Bayesian VAR Models in the Presence of Collinearity and Autocorrelated Error Terms

Open Journal of Statistics ◽

10.4236/ojs.2016.61012 ◽

2016 ◽

Vol 06 (01) ◽

pp. 96-132

Author(s):

M. O. Adenomon ◽

V. A. Michael ◽

O. P. Evans

Keyword(s):

Var Models ◽

Bayesian Var ◽

Autocorrelated Error ◽

Error Terms

Download Full-text

A Note on Confidence Intervals and Model Specification

Marketing ZFP ◽

10.15358/0344-1369-2019-4-33 ◽

2019 ◽

Vol 41 (4) ◽

pp. 33-42

Author(s):

Thomas Otter

Keyword(s):

Confidence Intervals ◽

Generalized Linear Model ◽

Statistical Information ◽

Regression Coefficients ◽

Model Specification ◽

Model Parameters ◽

Exploratory Research ◽

Empirical Calibration ◽

Credible Intervals ◽

Model Structures

Empirical research in marketing often is, at least in parts, exploratory. The goal of exploratory research, by definition, extends beyond the empirical calibration of parameters in well established models and includes the empirical assessment of different model specifications. In this context researchers often rely on the statistical information about parameters in a given model to learn about likely model structures. An example is the search for the 'true' set of covariates in a regression model based on confidence intervals of regression coefficients. The purpose of this paper is to illustrate and compare different measures of statistical information about model parameters in the context of a generalized linear model: classical confidence intervals, bootstrapped confidence intervals, and Bayesian posterior credible intervals from a model that adapts its dimensionality as a function of the information in the data. I find that inference from the adaptive Bayesian model dominates that based on classical and bootstrapped intervals in a given model.

Download Full-text

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

Iraqi Journal of Economic Sciences ◽

10.31272/ijes2020.66.6 ◽

2020 ◽

Vol 2020 (66) ◽

pp. 101-110

Author(s):

. Azhar Kadhim Jbarah ◽

Prof Dr. Ahmed Shaker Mohammed

Keyword(s):

Regression Model ◽

Moving Average ◽

Estimation Method ◽

Estimation Methods ◽

Autoregressive Moving Average ◽

Moving Average Models ◽

Error Terms ◽

Relationship Of ◽

The Relationship ◽

Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

Download Full-text

Regression analysis of indicators of a numerical experiment to determine the cooling time of oil

Oil and Gas Studies ◽

10.31660/0445-0108-2020-2-89-97 ◽

2020 ◽

pp. 89-97

Author(s):

A. U. Yakupov ◽

D. A. Cherentsov ◽

K. S. Voronin ◽

Yu. D. Zemenkov

Keyword(s):

Regression Analysis ◽

Computer Experiment ◽

Calculation Model ◽

Cooling Time ◽

Regression Coefficients ◽

Fisher Criterion ◽

Empirical Relationships ◽

Factors Affecting ◽

Oil Pipeline ◽

Significant Regression

The article performed the processing of the results of a computer experiment to determine the cooling time of oil in a stopped oil pipeline. We proposed a calculation model in previous works that allows you to simulate the process of cooling oil.There was a need to verify the previously obtained results when conducting a laboratory experiment on a stand with soil. To conduct the experiment, it was necessary to conduct the planning of the experiment. The factors affecting the cooling time of oil in the oil pipeline, which will vary in the proposed experiment, are determined, empirical relationships are established. A regression analysis was carried out, and the dispersion homogeneity was checked using the Cochren criterion. The estimates of reproducibility variances are calculated. The adequacy hypothesis was tested using the Fisher criterion. Significant regression coefficients are established.

Download Full-text