New Innovative Method in the Field of Social Choice Theory
Zoïnabo Savadogo,
Sougoursi Jean Yves Zaré,
Wambie Zongo,
Somdouda Sawadogo,
Blaise Somé
Issue:
Volume 10, Issue 6, December 2021
Pages:
121-126
Received:
22 October 2021
Accepted:
19 November 2021
Published:
27 November 2021
Abstract: Social choice theory includes the study of voting methods. In the literature on social choice theory many methods exist, the main objective of all these methods is the determination of a good method. However, many of these methods give controversial results which often lead to disputes. It should also be noted that sometimes, regardless of the method used, there are people who are not ready to accept the results given by the ballot box. The ideal would be to find a method with good properties, because it seems that there are no completely satisfactory methods. Since the goal of a voting method is to reconcile several points of view into a general interest, one should focus on the properties. The geometric mean does not lead to a compensation of weak criteria by stronger ones as it is the case with the arithmetic mean. Indeed, by using the geometric mean, even if only one criterion is very weak and the others are very strong, a candidate may not be well ranked; moreover, assent voting is very well appreciated in the literature by many authors and also generates huge opportunities. This justifies our choice in this work to combine geometric mean and assent voting to develop a method with good properties.
Abstract: Social choice theory includes the study of voting methods. In the literature on social choice theory many methods exist, the main objective of all these methods is the determination of a good method. However, many of these methods give controversial results which often lead to disputes. It should also be noted that sometimes, regardless of the meth...
Show More
Fitting Spatial Joint Model for U.S Regional Influenza-like Illness (ILINet) Data Set
Azizur Rahman,
Arifa Tabassum,
Mariam Akter
Issue:
Volume 10, Issue 6, December 2021
Pages:
127-138
Received:
25 October 2021
Accepted:
13 November 2021
Published:
29 December 2021
Abstract: Background: Influenza is commonly known as the flu, which is a viral infectious disease that attacks our respiratory systems, such as the nose, throat, and lungs. Several studies have been performed on influenza determinants, concentrating on the role of biological and behavioral risk factors at the personal level to reduce the burden of the disease. However, few studies conducted to identify geographical patterns of infectious disease and its associated factors. Objective: This study aimed to provide a step-by-step process of finding the geographic patterns of influenza cases and the role that they can be determined by the racial factor. Method: In this study, first non-spatial and spatial models were estimated, and then a step-by-step procedure was used to fit a spatial joint model to the US Influenza Like Illness (ILINet) dataset using a single predictor: percentage of African American people in each state. Results: Findings revealed that for both non-spatial and spatial models, the racial variable was positively associated with standard morbidity ratio (SMR) and was highly statistically significant (p<0.0001). In addition, it showed that there was a large residual spatial dependency for the spatial joint model, which meant for our dataset, the spatial component explained much of the variability. Conclusion: Researchers that desire to create a joint special model from the ground up in the instance of infectious illness modelling can benefit from this research.
Abstract: Background: Influenza is commonly known as the flu, which is a viral infectious disease that attacks our respiratory systems, such as the nose, throat, and lungs. Several studies have been performed on influenza determinants, concentrating on the role of biological and behavioral risk factors at the personal level to reduce the burden of the diseas...
Show More
A Modified Breusch–Pagan Test for Detecting Heteroscedasticity in the Presence of Outliers
Bolakale Abdul-Hameed,
Oyeyemi Gafar Matanmi
Issue:
Volume 10, Issue 6, December 2021
Pages:
139-149
Received:
1 November 2021
Accepted:
26 November 2021
Published:
29 December 2021
Abstract: Heteroscedasticity is a problem that arises in regression analysis for a variety of causes. This problem impacts both the estimation and test procedures and it is therefore critical to be able to detect the problem and address it. The presence of outliers is a regular occurrence in data analysis and the detection of heteroscedasticity in the presence of outliers poses lots of difficulty for most of the existing methods. In this paper, a modified Breusch-Pagan test for heteroscedasticity in the presence of outliers was proposed. The modified test is obtained by substituting non-robust components in the Breusch-Pagan test with robust procedures which makes the modified Breusch-Pagan test to be unaffected by outliers. Monte Carlo simulations and real data sets were used to investigate the performance of the newly proposed test. The probability value (p–value) and power of all methods considered in this study were computed and the results indicate that the modified robust version of Breusch-Pagan test outperformed the previous tests significantly. The proposed modified Breusch-Pagan test is therefore recommended for testing for heteroscedasticity in linear regression diagnosis, especially when the data sets evidently contain outliers.
Abstract: Heteroscedasticity is a problem that arises in regression analysis for a variety of causes. This problem impacts both the estimation and test procedures and it is therefore critical to be able to detect the problem and address it. The presence of outliers is a regular occurrence in data analysis and the detection of heteroscedasticity in the presen...
Show More
