Inference on log-linear regression model parameters with composite autocorrelated errors

2015 ◽  
Vol 10 (3) ◽  
pp. 231-242
Author(s):  
Late Anis Chandra Mukhopadhyay ◽  
Rabindra Nath Das
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model Parameters ◽  
Log Linear ◽  
Autocorrelated Errors

scholarly journals Solving Multicollinearity Problem in Linear Regression Model: The Review Suggests New Idea of Partitioning and Extraction of the Explanatory Variables

2021 ◽  
Vol 2 (1) ◽  
pp. 12-20
Author(s):  
Kayode Ayinde, Olusegun O. Alabi ◽  
Ugochinyere Ihuoma Nwosu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Real Data ◽  
Least Square ◽  
Model Parameters ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Wide Range ◽  
High Level

Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused on developing estimators regardless of effect of differences in levels of multicollinearity among regressors. Studies have considered single-estimator and combined-estimator approaches without sustainable solution to multicollinearity problems. The possible influence of partitioning the regressors according to multicollinearity levels and extracting from each group to develop estimators that will estimate the parameters of a linear regression model when multicollinearity occurs is a new econometrics idea and therefore requires attention. The results of new studies should be compared with existing methods namely principal components estimator, partial least squares estimator, ridge regression estimator and the ordinary least square estimators using wide range of criteria by ranking their performances at each level of multicollinearity parameter and sample size. Based on a recent clue in literature, it is possible to develop innovative estimator that will sustainably solve the problem of multicollinearity through partitioning and extraction of explanatory variables approaches and identify situations where the innovative estimator will produce most efficient result of the model parameters. The new estimator should be applied to real data and popularized for use.

scholarly journals Consistency of Konker-Bassett estimators in linear regression model

2018 ◽  
pp. 17-24
Author(s):  
O. Ivanov ◽  
N. Kaptur ◽  
I. Savych
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Random Noise ◽  
Observation Time ◽  
Stationary Time Series ◽  
Model Parameters ◽  
Local Transformation ◽  
Stationary Time

Asymptotic properties of Koenker - Bassett estimators of linear regression model parameters with discrete observation time and random noise being nonlinear local transformation of Gaussian stationary time series with singular spectrum are studied. The goal of the work lies in obtaining the requirements to regression function and time series that simulates the random noise, under which the Koenker - Bassett estimators of regression model parameters are consistent. Linear regression model with discrete observation time and bounded open convex parametric set is the object of the studying. For the first time in linear regression model with described stationary time series as noise having singular spectrum, the weak consistency of unknown parameters Koenker - Bassett estimators are obtained. For getting these results complicated concepts of time series theory and time series statistics have been used, namely: local transformation of Gaussian stationary time series, stationary time series with singular spectral density, expansions by Chebyshev - Hermite polynomials of the transformed Gaussian time series values.

scholarly journals Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors

2021 ◽  
Vol 17 (33) ◽  
pp. 45-70
Author(s):  
Álvaro Alexander Burbano Moreno ◽  
Oscar Orlando Melo-Martinez ◽  
M Qamarul Islam
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Least Square ◽  
Model Parameters ◽  
Modified Maximum Likelihood ◽  
Test Of Hypothesis

We study multiple linear regression model under non-normally distributed random error by considering the family of generalized secant hyperbolic distributions. We derive the estimators of model parameters by using modified maximum likelihood methodology and explore the properties of the modified maximum likelihood estimators so obtained. We show that the proposed estimators are more efficient and robust than the commonly used least square estimators. We also develop the relevant test of hypothesis procedures and compared the performance of such tests vis-a-vis the classical tests that are based upon the least square approach.

A new bounded log-linear regression model

Metrika ◽  
2013 ◽  
Vol 77 (5) ◽  
pp. 695-720 ◽  
Author(s):  
HaiYing Wang ◽  
Nancy Flournoy ◽  
Eloi Kpamegan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Log Linear

Poisson Log-Linear Regression Model for Rural Signalized Intersection

2012 ◽  
Vol 594-597 ◽  
pp. 1391-1394
Author(s):  
Zhi Ping Ren
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Traffic Safety ◽  
Linear Regression Model ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Left Turn ◽  
Traffic Volumes ◽  
Input Variables ◽  
Log Linear

Safety performance of rural signalized intersections is critical for identifying high-risk sites and predicting the hazardousness. This paper aims to develop a predictive model that will describe the safety of rural signalized intersections based on various input variables. Data are examined from 124 rural signalized intersections over three states, and Poisson log-linear regression model is presented, which connected traffic number and the average traffic volumes, geometric characteristics and signalization characteristics variables together. The model and associated data analysis reveal that average daily traffic, media width, speed limit, degree of horizontal curvature and left-turn lane are the factors that have greatest overall effect on safety. The results show that the Poisson log-linear regression model is able to describe the rural signalized intersection safety accurately. Using this model, effective countermeasures can be applied for improving traffic safety.

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