scholarly journals Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression

2019 ◽  
Vol 11 (7) ◽  
pp. 2071 ◽  
Author(s):  
Yanyong Guo ◽  
Yao Wu ◽  
Jian Lu ◽  
Jibiao Zhou
Keyword(s):  
Risk Factors ◽  
Unobserved Heterogeneity ◽  
Multinomial Logit ◽  
Model Parameters ◽  
License Plate ◽  
Speed Limit ◽  
Random Parameters ◽  
Logit Regression ◽  
Multinomial Logit Regression ◽  
The Impact

Understanding the risk factors of e-bike collisions can improve e-bike riders’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates risk factors that significantly contribute to the severity of e-bike collisions. Two months of e-bike collision data were collected in the city of Ningbo, China. A random parameters multinomial logit regression (RP-MNL) is proposed to account for the unobserved heterogeneity across observations. A fixed parameters multinomial logit regression (FP-MNL) is estimated and compared with the RP-MNL under the Bayesian framework. The full Bayesian approach based on Markov chain Monte Carlo simulation is employed to estimate the model parameters. Both parameter estimates and odds ratio (OR) are used to interpret the impact of risk factors on the severity of e-bike collisions. The model comparison results show that RP-MNL outperforms FP-MNL, indicating that accommodating the unobserved heterogeneity across observations could improve the model fit. The model estimation results show that age, gender, e-bike behavior, license plate, bicycle type, location, and speed limit are statistically significant and associated with the severity of e-bike collisions. Furthermore, four risk factors, i.e., gender, e-bike behavior, bicycle type, and speed limit, are found to have heterogeneous effects on severity of e-bike collisions, appearing in the form of random parameters in the statistical model.

scholarly journals The Impact of Market Outlets Choice on Beekeepers’ Income in Rural Poor: A Case Study of Badakhshan Province

2021 ◽  
Author(s):  
Jamshid Yolchi
Keyword(s):  
Family Size ◽  
Multinomial Logit ◽  
Rural Poor ◽  
Factors Affecting ◽  
Logit Regression ◽  
Multinomial Logit Regression ◽  
The Government ◽  
The Impact

This research carried out to uncover the effect of beekeeping on the income of rural poor and to which extent that market outlet choice affects the income of beekeepers. The findings of Multinomial Logit regression, from 129 questionnaires of 4 districts indicate that there is no relationship between market outlet choice and income of beekeepers. The income of beekeepers is mostly affected by their family size and working experience. But the factors affecting to choose the home selling market outlet is very different from those of three other channels. In order to promote the income of beekeepers, it’s recommended that the government and other involved NGOs work on arrangements on wholesale opportunities for beekeepers. Because over 102 out of 129 samples have indicated that their products aren’t sold out on time. It means that there is a huge opportunity of filling the gap of honey demand in Afghanistan by promoting the links between producers and buyers.

scholarly journals The Impact of Market Outlets Choice on Beekeepers’ Income in Rural Poor: A Case Study of Badakhshan Province

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Jamshid Yolchi ◽  
Keyword(s):  
Family Size ◽  
Multinomial Logit ◽  
Rural Poor ◽  
Factors Affecting ◽  
Logit Regression ◽  
Multinomial Logit Regression ◽  
The Government ◽  
The Impact

This research carried out to uncover the effect of beekeeping on the income of rural poor and to which extent that market outlet choice affects the income of beekeepers. The findings of Multinomial Logit regression, from 129 questionnaires of 4 districts indicate that there is no relationship between market outlet choice and income of beekeepers. The income of beekeepers is mostly affected by their family size and working experience. But the factors affecting to choose the home selling market outlet is very different from those of three other channels. In order to promote the income of beekeepers, it’s recommended that the government and other involved NGOs work on arrangements on wholesale opportunities for beekeepers. Because over 102 out of 129 samples have indicated that their products aren’t sold out on time. It means that there is a huge opportunity of filling the gap of honey demand in Afghanistan by promoting the links between producers and buyers.

Who Makes a Compromise? Adopting Pledges in Czech Coalition Agreements

2020 ◽  
Vol 28 (5) ◽  
pp. 728-743
Author(s):  
Petra Vodová
Keyword(s):  
Statistical Analysis ◽  
Multinomial Logit ◽  
Issue Salience ◽  
Logit Regression ◽  
Multinomial Logit Regression

Translating party pledges into coalition agreements is a crucial goal of after-election coalition negotiations. Full adoption is the best result for the bargaining party, while limited adoption is a kind of compromise forced by coalition partners, and non-adoption can be seen as a defeat. The question of what undermines the compromise and defeat in coalition agreements is, however, rarely answered. This article formulates hypotheses concerning the effect of consensual pledges among coalition parties, and party and voter-issue salience on parties’ ability to adopt their pledges and adopt them fully or partially. The effect of party level characteristics is considered. The analysis is provided on a new dataset of narrow Czech coalition party pledges in three governments established after elections in 2006, 2010 and 2013. Multinomial logit regression is used for the statistical analysis.

scholarly journals IMPACT OF A PREVIOUS AUDIT ON TAX AGGRESSIVENESS OF A FIRM TAXPAYER

2019 ◽  
Vol 1 (3) ◽  
pp. 15
Author(s):  
Ivan Rona Penata
Keyword(s):  
Multinomial Logit ◽  
Effective Tax Rate ◽  
Tax Rate ◽  
Tax Aggressiveness ◽  
Logit Regression ◽  
Multinomial Logit Regression ◽  
Audit Result ◽  
Tax Audit ◽  
Return Data

This study aims to analyze the effect of a previous tax audit on tax aggressiveness of a firm taxpayer who submits Overpayment Annual Tax Return. The degree of tax aggressiveness itself uses Delta Effective Tax Rate as a proxy, generated from Annual Tax Return data from 2011 to 2016. Using multinomial logit regression as a method, this study found that a previous Tax Audit and tax audit result made a firm prefer to choose a positive Delta Effective Tax Rate.

scholarly journals Wage mobility in Europe. A comparative analysis using restricted multinomial logit regression

2008 ◽  
Vol 44 (1) ◽  
pp. 115-129 ◽  
Author(s):  
Dimitris Pavlopoulos ◽  
Ruud Muffels ◽  
Jeroen K. Vermunt
Keyword(s):  
Comparative Analysis ◽  
Multinomial Logit ◽  
Logit Regression ◽  
Multinomial Logit Regression

scholarly journals Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model

2020 ◽  
Vol 17 (4) ◽  
pp. 1259 ◽  
Author(s):  
Changxi Ma ◽  
Jibiao Zhou ◽  
Dong Yang
Keyword(s):  
Influence Factors ◽  
Hazardous Materials ◽  
Model Parameters ◽  
Ordered Logit ◽  
Road Transportation ◽  
Hazardous Materials Transportation ◽  
Logit Regression ◽  
Environmental Properties ◽  
The Impact ◽  
Causation Analysis

Understanding the influence factors and related causation of hazardous materials can improve hazardous materials drivers’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates the statistical distribution characteristics, such as types of hazardous materials transportation accidents, driver properties, vehicle properties, environmental properties, road properties. In total, 343 data regarding hazardous materials accidents were collected from the chemical accident information network of China. An ordered logit regression (OLR) model is proposed to account for the unobserved heterogeneity across observations. Four independent variables, such as hazardous materials drivers’ properties, vehicle properties, environmental properties, and road properties are employed based on the OLR model, an ordered multinomial logistic regression (MLR) is estimated the OLR model parameters. Both parameter estimates and odds ratio (OR) are employed to interpret the impact of influence factors on the severity of hazardous materials accidents. The model estimation results show that 10 factors such as violations, unsafe driving behaviors, vehicle faults, and so on are closely related to accidents severity of hazardous materials transportation. Furthermore, three enforcement countermeasures are proposed to prevent accidents when transporting hazardous materials.

Analysis of Crash Rates at Freeway Diverge Areas using Bayesian Tobit Modeling Framework

2019 ◽  
Vol 2673 (4) ◽  
pp. 652-662 ◽  
Author(s):  
Yanyong Guo ◽  
Zhibin Li ◽  
Tarek Sayed
Keyword(s):  
Risk Factors ◽  
Tobit Model ◽  
Information Criteria ◽  
Random Parameters ◽  
Modeling Framework ◽  
Crash Rates ◽  
Tobit Models ◽  
Heterogeneous Effects ◽  
The Impact ◽  
Diverge Areas

The goal of this study is to evaluate the impact of various risk factors on crash rates at freeway diverge areas. Crash rates data for a three-year period from 367 freeway diverge areas were used for analysis. Four candidate Tobit models were developed and compared under the Bayesian framework: a traditional Tobit model; a random parameters Tobit (RP-Tobit) model; a grouped random parameters Tobit (GRP-Tobit) model; and a random intercept Tobit (RI-Tobit). The results showed that the RP-Tobit model performs best with highest value of Rd2 as well as lowest Mean Absolute Deviance (MAD) and Deviance Information Criteria (DIC), indicating the importance of accounting for unobserved heterogeneity to improve the model fit. Both the GRP-Tobit and the RI-Tobit models provide better performance than the traditional Tobit model. The model results showed that crash rates at freeway diverge areas were positively associated with mainline annual average daily traffic (AADT) and negatively associated with ramp AADT, indicating the different mechanisms of the impact of traffic volume on crash rates at freeway diverge areas. Lane-balanced design and high speed limits at freeway diverge areas have a negative effect on crash rates. The number of lanes on mainline and ramp length have significant heterogeneous effects on crash rates across observations. The RP-Tobit model provides a more comprehensive understanding of the heterogeneous effects of risk factors on crash rates across observations.

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