Predicting automobile claims bodily injury severity with sequential ordered logit models

2007 ◽  
Vol 41 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Mercedes Ayuso ◽  
Miguel Santolino
Keyword(s):  
Injury Severity ◽  
Logit Models ◽  
Ordered Logit ◽  
Bodily Injury

scholarly journals Intention to move and residential satisfaction: evidence from Poland

Equilibrium ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 341-360
Author(s):  
Piotr Maleszyk ◽  
Arleta Kędra
Keyword(s):  
International Migration ◽  
Life Stage ◽  
Value Added ◽  
Residential Satisfaction ◽  
Policy Implications ◽  
Urban Migration ◽  
Logit Models ◽  
Ordered Logit ◽  
Financial Situation ◽  
Intention To Move

Research background: Residential mobility affects the spatial structure of cities and urban development. Longer-distance migration has many additional implications: it affects the demographic situation of a sending area as well as its growth prospects. The literature on interregional and especially international migration regards residential satisfaction as being of at least secondary importance. More attention to this concept is given in research on intra-urban migration and suburbanisation. In a seminal paper of Speare (1974), residential satisfaction was found to be the best predictor of the willingness to move. However, determinants of mobility are country-specific. Purpose of the article: Answering the following research questions: 1) What is the scale and selectivity of the intention to move among city residents? 2) Does residential satisfaction explain variation in migration intentions? Methods: The data are derived from the PAPI survey on life quality in Lublin, Poland (sample: 1101 residents). We build ordered logit models explaining residents’ declarations regarding different types of migration (intra-urban migration, suburbanisation, interregional and international migration) with various proxies of residential satisfaction, as well as financial situation and demographic attributes. Findings & Value added: The propensity to migrate was declared by approx. 15–30% of respondents, depending on the type of migration, which indicates relatively low mobility as against EU countries. We confirm that the intention to move is highly selective. The estimated ordered logit models explaining the intention to move prove that satisfaction with housing and neighbourhood characteristics along with life-stage characteristics are relevant predictors of intention to move both within and outside the region. We disregard the opinion that unemployment and adverse financial situation are key drivers of mobility in contemporary Poland. In a more international context, we provide evidence on how long- and short-distance migration are different in nature and discuss some policy implications regarding countering depopulation in peripheral areas.

scholarly journals Injury Severity of Motorcycle Riders Involved in Traffic Crashes in Hunan, China: A Mixed Ordered Logit Approach

2016 ◽  
Vol 13 (7) ◽  
pp. 714 ◽  
Author(s):  
Fangrong Chang ◽  
Maosheng Li ◽  
Pengpeng Xu ◽  
Hanchu Zhou ◽  
Md. Haque ◽  
...  
Keyword(s):  
Injury Severity ◽  
Ordered Logit ◽  
Traffic Crashes

Bayesian Approaches to Assessing the Parallel Lines Assumption in Cumulative Ordered Logit Models

2019 ◽  
pp. 004912411988246
Author(s):  
Jun Xu ◽  
Shawn G. Bauldry ◽  
Andrew S. Fullerton
Keyword(s):  
Health Research ◽  
Logit Model ◽  
Logit Models ◽  
Ordered Logit ◽  
Bayesian Approaches ◽  
Parallel Lines ◽  
Cumulative Logit Model ◽  
Cumulative Logit ◽  
Modeling Techniques ◽  
Null Values

We first review existing literature on cumulative logit models along with various ways to test the parallel lines assumption. Building on the traditional frequentist framework, we introduce a method of Bayesian assessment of null values to provide an alternative way to examine the parallel lines assumption using highest density intervals and regions of practical equivalence. Second, we propose a new hyperparameter cumulative logit model that can improve upon existing ones in addressing several challenges where traditional modeling techniques fail. We use two empirical examples from health research to showcase the Bayesian approaches.

Pedestrian Injury Severities in Pedestrian-Vehicle Crashes and the Partial Proportional Odds Logit Model: Accounting for Age Difference

2019 ◽  
Vol 2673 (5) ◽  
pp. 731-746 ◽  
Author(s):  
Yang Li ◽  
Wei (David) Fan
Keyword(s):  
Logit Model ◽  
Injury Severity ◽  
Parameter Estimates ◽  
Age Difference ◽  
Age Group ◽  
Vehicle Crashes ◽  
Ordered Logit ◽  
Proportional Odds ◽  
Pedestrian Injury ◽  
Injury Severities

This study investigates factors that significantly contribute to the severity of pedestrian injuries resulting from pedestrian-vehicle crashes. Multinomial logit (MNL) models, mixed logit (ML) models, and ordered logit/probit models have been widely used in modeling crash injury severity, including pedestrian injury severity in pedestrian-vehicle crashes. However, both MNL and ML models treat injury severity levels as non-ordered, ignoring the inherent hierarchical nature of crash injury severities, and the data used in ordered logit models need to be strictly subjected to the proportional odds (PO) assumption. In this study, a partial proportional odds (PPO) logit model approach is employed to explore the issues of pedestrian safety associated with each age group: young (aged under 24), middle-aged (aged 25–55), and older pedestrians (aged over 55). Data used in this study are police-reported pedestrian crash data collected from 2007 to 2014 in North Carolina. A variety of motorist, pedestrian, environmental, and roadway characteristics are inspected. Results from likelihood ratio tests statistically show the better performance of developing separate injury severity models for each age group compared with estimating a single model utilizing all data. Relevant parameter estimates and associated marginal effects are used to interpret the results, followed by recommendations made in the concluding section.

scholarly journals feologit: A new command for fitting fixed-effects ordered logit models

2020 ◽  
Vol 20 (2) ◽  
pp. 253-275 ◽  
Author(s):  
Gregori Baetschmann ◽  
Alexander Ballantyne ◽  
Kevin E. Staub ◽  
Rainer Winkelmann
Keyword(s):  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Logit Models ◽  
Ordered Logit ◽  
The Social ◽  
The Standard Model ◽  
Dependent Variables ◽  
Using Data ◽  
New Community ◽  
Time Invariant

In this article, we describe how to fit panel-data ordered logit models with fixed effects using the new community-contributed command feologit. Fixed-effects models are increasingly popular for estimating causal effects in the social sciences because they flexibly control for unobserved time-invariant heterogeneity. The ordered logit model is the standard model for ordered dependent variables, and this command is the first in Stata specifically for this model with fixed effects. The command includes a choice between two estimators, the blowup and cluster (BUC) estimator introduced in Baetschmann, Staub, and Winkelmann (2015, Journal of the Royal Statistical Society, Series A 178: 685–703) and the BUC- τ estimator in Baetschmann (2012, Economics Letters 115: 416–418). Baetschmann, Staub, and Winkelmann (2015) showed that the BUC estimator has good properties and is almost as efficient as more complex estimators such as generalized method-of-moments and empirical likelihood estimators. The command and model interpretations are illustrated with an analysis of the effect of parenthood on life satisfaction using data from the German Socio-Economic Panel.

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