copula-based binary logit-linear regression model (Eluru Municipal)
90
Structured Review
Eluru Municipal
copula-based binary logit-linear regression model
Copula Based Binary Logit Linear Regression Model, supplied by Eluru Municipal, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/copula-based+binary+logit-linear+regression+model/copula+approach/pmc07314698-270-35-44
Average 90 stars, based on 1 article reviews
Copula Based Binary Logit Linear Regression Model, supplied by Eluru Municipal, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/copula-based+binary+logit-linear+regression+model/copula+approach/pmc07314698-270-35-44
Average 90 stars, based on 1 article reviews
copula-based binary logit-linear regression model - by Bioz Stars,
2026-09
90/100 stars
Images
Related Articles
other:Article Title: Jointly modeling the dependence of injury severity and crash size involved in motorcycle crashes in Cambodia using a copula-based approach Article Snippet: Escalating motorcycle crashes present a significant challenge due to the increase in motorcycle registrations and the corresponding increase in mortality rates.. This issue is particularly acute in Cambodia, where motorcycles are the primary mode of transportation.. In the analysis of motorcycle crashes, two key measures of severity are injury severity and crash size, notably the number of injuries. Article Title: Assessing non-motorist safety in motor vehicle crashes – a copula-based approach to jointly estimate crash location type and injury severity Article Snippet: Non-motorist injury severity can be affected by various observed and unobserved attributes related to the crash location type (segment or intersection).. Recognizing the distinct non-motorist injury severity profiles by crash location type, we propose a joint modeling framework to study crash location type and non-motorist injury severity as two dimensions of the severity process.. We employ a copula-based joint framework that ties the crash location type (represented as a binary logit model) and injury severity (represented as a generalized ordered logit model) through a closed form flexible dependency structure to study the injury severity process. Article Title: Copula-based bivariate count data regression models for simultaneous estimation of crash counts based on severity and number of vehicles. Article Snippet: Statistical models of crash frequency typically apply univariate regression models to estimate total crash frequency or crash counts by various categories.. However, a possible correlation between the dependent variables or unobserved variables associated with the dependent variables is not considered when univariate models are used to estimate categorized crash counts—such as different severity levels or numbers of vehicles involved.. This may lead to inefficient parameter estimates compared to multivariate models that directly consider these correlations. Article Title: Is the front passenger seat always the "death seat"? An application of a hierarchical ordered probit model for occupant injury severity. Article Snippet: Although many studies have investigated the correlations between injury severities and seat positions, few researchers explored the correlates of injury severities (e.g., seat positions) within a crash that results in multiple occupant injuries.. Therefore, we examine the injury correlates within and between crashes, and study the correlations between seat positions and occupant injury severity by constructing a hierarchical ordered probit model. A total of 20,327 occupant injuries in 16,405 motor vehicle crashes in South Australia (2012 2016) are used.. The results of this study indicate that the rear left passenger seat is associated with a 7.66% higher chance of getting injured (including moderate and severe injury), and the front left passenger seat is associated with a 2.94% higher chance of getting injured compared with the driver seat. Article Title: A semi-parameter copula model for vehicle damage severity in lane-changing related crashes. Article Snippet: Lane changing behaviour occurs frequently on the highways.. However, it also poses a major impact on traffic operation and safety since complex interactions between two or more vehicles on different traffic lanes are involved.. In the lane-changing related crashes, correlation in damage level among the vehicles involved is prevalent. Article Title: A random parameter bivariate probit model for injury severities of riders and pillion passengers in motorcycle crashes Article Snippet: This study proposes a random parameter bivariate probit model to analyze risk factors on the crash injury severity of both motorcycle riders and passengers in a single modeling framework.. The proposed model can not only account for the underlying correlation of common factors affecting the rider and its pillion passenger simultaneously, but also can capture the unobserved heterogeneity across crash samples.. The case analysis is based on 3665 motorcycle-carrying-passenger crashes in Hunan province of China. Article Title: Built environment, driving errors and violations, and crashes in naturalistic driving environment. Article Snippet: Driving errors and violations are highly relevant to the safe systems approach as human errors tend to be a predominant cause of crash occurrence.. In this study, we harness highly detailed pre-crash Naturalistic Driving Study (NDS) data 1) to understand errors and violations in crash, near-crash, and baseline (no event) driving situations, and 2) to explore pathways that lead to crashes in diverse built environments by applying rigorous modeling techniques.. The “locality” factor in the NDS data provides information on various types of roadway and environmental surroundings that could influence traffic flow when a precipitating event is observed. Article Title: Joint distribution modelling of vehicle dynamic parameters using copula Article Snippet: Joint distribution modelling of vehicle dynamic parameters using copula Geetimukta Mahapatra, Sanhita Das & Akhilesh Kumar Maurya To cite this article: Geetimukta Mahapatra, Sanhita Das & Akhilesh Kumar Maurya (2021): Joint distribution modelling of vehicle dynamic parameters using copula, Transportation Letters, DOI: 10.1080/19427867.2021.1897936 To link to this article: https://doi.org/10.1080/19427867.2021.1897936 |