The Performance of Diagnostic Tests for Spatial Dependence in Linear Regression Models: A Meta-Analysis of Simulation Studies

2004 ◽  
pp. 29-65 ◽  
Author(s):  
Raymond J. G. M. Florax ◽  
Thomas de Graaff
Keyword(s):  
Linear Regression ◽  
Spatial Dependence ◽  
Diagnostic Tests ◽  
Regression Models ◽  
Meta Analysis ◽  
Simulation Studies ◽  
Linear Regression Models

scholarly journals Robust Bayesian Regression with Synthetic Posterior Distributions

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 661 ◽  
Author(s):  
Shintaro Hashimoto ◽  
Shonosuke Sugasawa
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Bayesian Variable Selection ◽  
Bayesian Regression ◽  
Simulation Studies ◽  
Posterior Distributions ◽  
Linear Regression Models ◽  
Computation Algorithm ◽  
Shrinkage Priors ◽  
Variable Selection And Estimation

Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not necessarily straightforward. We here propose a Bayesian approach to robust inference on linear regression models using synthetic posterior distributions based on γ-divergence, which enables us to naturally assess the uncertainty of the estimation through the posterior distribution. We also consider the use of shrinkage priors for the regression coefficients to carry out robust Bayesian variable selection and estimation simultaneously. We develop an efficient posterior computation algorithm by adopting the Bayesian bootstrap within Gibbs sampling. The performance of the proposed method is illustrated through simulation studies and applications to famous datasets.

scholarly journals Quasi-Likelihood Ratio Tests for Homoscedasticity in Linear Regression

2020 ◽  
Vol 18 (1) ◽  
pp. 2-16
Author(s):  
Lili Yu ◽  
Varadan Sevilimedu ◽  
Robert Vogel ◽  
Hani Samawi
Keyword(s):  
Linear Regression ◽  
Likelihood Ratio ◽  
Regression Models ◽  
Likelihood Ratio Tests ◽  
Simulation Studies ◽  
Linear Regression Models

Two quasi-likelihood ratio tests are proposed for the homoscedasticity assumption in the linear regression models. They require few assumptions than the existing tests. The properties of the tests are investigated through simulation studies. An example is provided to illustrate the usefulness of the new proposed tests.

scholarly journals Homoscedasticity: an overlooked critical assumption for linear regression

2019 ◽  
Vol 32 (5) ◽  
pp. e100148
Author(s):  
Kun Yang ◽  
Justin Tu ◽  
Tian Chen
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Linear Regression ◽  
Regression Models ◽  
Data Distribution ◽  
The Other ◽  
Simulation Studies ◽  
Linear Regression Models ◽  
Popular Belief

Linear regression is widely used in biomedical and psychosocial research. A critical assumption that is often overlooked is homoscedasticity. Unlike normality, the other assumption on data distribution, homoscedasticity is often taken for granted when fitting linear regression models. However, contrary to popular belief, this assumption actually has a bigger impact on validity of linear regression results than normality. In this report, we use Monte Carlo simulation studies to investigate and compare their effects on validity of inference.

scholarly journals Erratum to: Standardizing effect size from linear regression models with log-transformed variables for meta-analysis

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Miguel Rodríguez-Barranco ◽  
Aurelio Tobías ◽  
Daniel Redondo ◽  
Elena Molina-Portillo ◽  
María José Sánchez
Keyword(s):  
Linear Regression ◽  
Effect Size ◽  
Regression Models ◽  
Meta Analysis ◽  
Linear Regression Models

scholarly journals Standardizing effect size from linear regression models with log-transformed variables for meta-analysis

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Miguel Rodríguez-Barranco ◽  
Aurelio Tobías ◽  
Daniel Redondo ◽  
Elena Molina-Portillo ◽  
María José Sánchez
Keyword(s):  
Linear Regression ◽  
Effect Size ◽  
Regression Models ◽  
Meta Analysis ◽  
Linear Regression Models

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.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued
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