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Panagiotis Ch Anastasopoulos - One of the best experts on this subject based on the ideXlab platform.
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A random thresholds random parameters hierarchical ordered Probit analysis of highway accident injury-severities
Analytic Methods in Accident Research, 2017Co-Authors: Grigorios Fountas, Panagiotis Ch AnastasopoulosAbstract:Abstract This study uses highway accident data collected in the State of Washington, between 2011 and 2013, to study the factors that affect accident injury-severities. To account for the fixed thresholds limitation of the traditional ordered probability Models – which typically leads to incorrect estimation of outcome probabilities for the intermediate categories – and for the possibility of unobserved factors systematically varying across the observations, a random thresholds hierarchical ordered Probit Model with random parameters is estimated. This approach simultaneously allows the explanatory parameters to vary across roadway segments, and the thresholds to vary both as a function of explanatory parameters and across the observations, thus accounting for unobserved and threshold heterogeneity, respectively. Using goodness-of-fit measures, likelihood ratio tests and forecasting accuracy measures, the Model estimation results are compared with the hierarchical and fixed thresholds ordered Probit Model counterparts, with fixed and random parameters. The comparative assessment among the ordered Probit Modeling approaches reveals the relative benefits and the overall statistical superiority of the random thresholds random parameters hierarchical ordered Probit Model.
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grouped random parameters bivariate Probit analysis of perceived and observed aggressive driving behavior a driving simulation study
Analytic Methods in Accident Research, 2017Co-Authors: Tawfiq Sarwar, Panagiotis Ch Anastasopoulos, Nima Golshani, Kevin F HulmeAbstract:Abstract This paper uses driving simulation data and surveys conducted in 2014 and 2015 in Buffalo, NY, to study the factors that affect perceived (self-reported, based on surveys) and observed (as measured, based on driving simulation experiments) aggressive driving behavior. Perceived and observed aggressive driving behavior are likely to share unobserved characteristics. To simultaneously account for this cross-equation error correlation, and for unobserved heterogeneity and panel data effects, a grouped random parameters bivariate Probit Model is estimated. The results control and account for a number of socio-demographic, driving experience and exposure, and behavioral and other characteristics. The findings reveal that different variables play in how aggressive driving behavior is perceived and observed, and the results imply that some drivers may perceive their driving behavior as non-aggressive when it is aggressive (or the opposite). The grouped random parameters bivariate Probit Model results are compared to their univariate Probit, full information maximum likelihood bivariate Probit, bivariate Probit Model with random effects, and random parameters bivariate Probit Model counterparts, and the results reveal the statistical superiority of the former, in terms of explanatory power, Model fit, and forecasting accuracy.
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comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered Probit Model
Analytic Methods in Accident Research, 2014Co-Authors: Brendan J Russo, Peter T Savolainen, William H Schneider, Panagiotis Ch AnastasopoulosAbstract:The extant traffic safety research literature includes numerous examples of studies that assess those factors affecting the degree of injury sustained by crash-involved motor vehicle occupants. One important methodological concern in such work is the potential correlation in injury outcomes among occupants involved in the same crash, which may be due to common unobserved factors affecting such occupants. A second concern is unobserved heterogeneity, which is reflective of parameter effects that vary across individuals and crashes. To address these concerns, a random parameters bivariate ordered Probit Model is estimated to examine factors affecting the degree of injury sustained by drivers involved in angle collisions. The Modeling framework distinguishes between the effects of relevant factors on the injury outcomes of the at-fault and not-at-fault parties. The methodological approach allows for consideration of within-crash correlation, as well as unobserved heterogeneity, and results in significantly improved fit as compared to a series of independent Models with fixed parameters. While the factors affecting injury severity are found to be similar for both drivers, the magnitudes of these effects vary between the at-fault and not-at-fault drivers. The results demonstrate that injury severity outcomes are correlated for drivers involved in the same crash. Further, the impacts of specific factors may be over- or under-estimated if such correlation is not accounted for explicitly as a part of the analysis. Various factors are found to affect driver injury severity and the random parameters framework shows these effects to vary across crashes and individuals. The analytical approach utilized provides a useful framework for injury severity analysis.
Mingtao Song - One of the best experts on this subject based on the ideXlab platform.
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investigation on the injury severity of drivers in rear end collisions between cars using a random parameters bivariate ordered Probit Model
International Journal of Environmental Research and Public Health, 2019Co-Authors: Feng Chen, Mingtao SongAbstract:The existing studies on drivers’ injury severity include numerous statistical Models that assess potential factors affecting the level of injury. These Models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered Probit Model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed Model outperforms the two separate ordered Probit Models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.
Jeffrey E Harris - One of the best experts on this subject based on the ideXlab platform.
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heterogeneous impact of the seguro popular program on the utilization of obstetrical services in mexico 2001 2006 a multinomial Probit Model with a discrete endogenous variable
Journal of Health Economics, 2009Co-Authors: Sandra G Sosarubi, Omar Galarraga, Jeffrey E HarrisAbstract:Objective We evaluated the impact of Seguro Popular (SP), a program introduced in 2001 in Mexico primarily to finance health care for the poor. We focused on the effect of household enrollment in SP on pregnant women's access to obstetrical services, an important outcome measure of both maternal and infant health.Data We relied upon data from the cross-sectional 2006 National Health and Nutrition Survey (ENSANUT) in Mexico. We analyzed the responses of 3890 women who delivered babies during 2001-2006 and whose households lacked employer-based health care coverage.Methods We formulated a multinomial Probit Model that distinguished between three mutually exclusive sites for delivering a baby: a health unit specifically accredited by SP; a non-SP-accredited clinic run by the Department of Health (Secretaria de Salud, or SSA); and private obstetrical care. Our Model accounted for the endogeneity of the household's binary decision to enroll in the SP program.Results Women in households that participated in the SP program had a much stronger preference for having a baby in a SP-sponsored unit rather than paying out of pocket for a private delivery. At the same time, participation in SP was associated with a stronger preference for delivering in the private sector rather than at a state-run SSA clinic. On balance, the Seguro Popular program reduced pregnant women's attendance at an SSA clinic much more than it reduced the probability of delivering a baby in the private sector. The quantitative impact of the SP program varied with the woman's education and health, as well as the assets and location (rural vs. urban) of the household.Conclusions The SP program had a robust, significantly positive impact on access to obstetrical services. Our finding that women enrolled in SP switched from non-SP state-run facilities, rather than from out-of-pocket private services, is important for public policy and requires further exploration.
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heterogeneous impact of the seguro popular program on the utilization of obstetrical services in mexico 2001 2006 a multinomial Probit Model with a discrete endogenous variable
National Bureau of Economic Research, 2007Co-Authors: Sandra G Sosarubi, Omar Galarraga, Jeffrey E HarrisAbstract:Objective: We evaluated the impact of Seguro Popular (SP), a program introduced in 2001 in Mexico primarily to finance health care for the poor. We studied the effect of SP on pregnant women's access to obstetrical services. Data: We analyzed the cross-sectional 2006 National Health and Nutrition Survey (ENSANUT), focusing on the responses of 3,890 women who delivered babies during 2001-2006 and whose households lacked employer-based health care coverage. Methods: We formulated a multinomial Probit Model that distinguished between three mutually exclusive sites for delivering a baby: a health unit accredited by SP; a clinic run by the Department of Health (Secretaria de Salud, or SSA); and private obstetrical care. Our Model accounted for the endogeneity of the household's binary decision to enroll in the SP program. Results: Women in households that participated in the SP program had a much stronger preference for having a baby in a SP-sponsored unit rather than paying out of pocket for a private delivery. At the same time, participation in SP was associated with a stronger preference for delivering in the private sector rather than at a state-run SSA clinic. On balance, the Seguro Popular program reduced pregnant women's attendance at an SSA clinic much more than it reduced the probability of delivering a baby in the private sector. The impacts of the SP program at the individual and population levels varied with the woman's education and health, as well as the assets and location (rural versus urban) of the household. Conclusions: The SP program had a robust, significantly positive impact on access to obstetrical services. Our finding that women enrolled in SP switched from non-SP state-run facilities, rather than from out-of-pocket private services, is important for public policy and requires further exploration.
Rosalba Radice - One of the best experts on this subject based on the ideXlab platform.
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estimation of a semiparametric recursive bivariate Probit Model in the presence of endogeneity
Canadian Journal of Statistics-revue Canadienne De Statistique, 2011Co-Authors: Giampiero Marra, Rosalba RadiceAbstract:The classic recursive bivariate Probit Model is of particular interest to researchers since it allows for the estimation of the treatment effect that a binary endogenous variable has on a binary outcome in the presence of unobservables. In this article, the authors consider the semiparametric version of this Model and introduce a Model fitting procedure which permits to estimate reliably the parameters of a system of two binary outcomes with a binary endogenous regressor and smooth functions of continuous covariates. They illustrate the empirical validity of the proposal through an extensive simulation study. The approach is applied to data from a survey, conducted in Botswana, on the impact of education on women's fertility. Some studies suggest that the estimated effect could have been biased by the possible endogeneity arising because unobservable confounders (e. g., ability and motivation) are associated with both fertility and education. The Canadian Journal of Statistics 39: 259-279; 2011 (C) 2011 Statistical Society of Canada
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testing exogeneity in the bivariate Probit Model a monte carlo study
Oxford Bulletin of Economics and Statistics, 2008Co-Authors: Chiara Monfardini, Rosalba RadiceAbstract:We conduct an extensive Monte Carlo experiment to examine the finite sample properties of maximum-likelihood-based inference in the bivariate Probit Model with an endogenous dummy. We analyse the relative performance of alternative exogeneity tests, the impact of distributional misspecification and the role of exclusion restrictions to achieve parameter identification in practice. The results allow us to infer important guidelines for applied econometric practice.
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testing exogeneity in the bivariate Probit Model a monte carlo study
Social Science Research Network, 2006Co-Authors: Chiara Monfardini, Rosalba RadiceAbstract:We conduct an extensive Monte Carlo experiment to examine the finite samples properties of maximum likelihood based inference in the bivariate Probit Model with endogenous dummy. We analyse the relative performance of alternative exogeneity tests, the impact of distributional misspecification and the role of exclusion restrictions to achieve parameter identification in practice. The results of our investigation allow us to draw some important guidelines for the applied econometric practice.
Rodrigo A Garrido - One of the best experts on this subject based on the ideXlab platform.
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port of destination and carrier selection for fruit exports a multi dimensional space time multi nomial Probit Model
Transportation Research Part B-methodological, 2004Co-Authors: Rodrigo A Garrido, Mabel A LevaAbstract:This papers studies the selection of carrier and destination port for Chilean fruit exporters. This double selection is analyzed as a stochastic choice process with time and space interactions. A space-time error structure was specified within a multi-nomial Probit Model, considering serial correlation, spatial autocorrelation, and state dependence. The Modeling approach was successfully applied to the case of grape exporters from Chile to the USA. The results validated the behavioral hypothesis for this process, i.e. there is significant state dependence, serial and spatial correlation in the choice of carrier and destination port, which should be considered when discrete choice Models are used to predict export flows.
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forecasting freight transportation demand with the space time multinomial Probit Model
Transportation Research Part B-methodological, 2000Co-Authors: Rodrigo A Garrido, Hani S. MahmassaniAbstract:Freight transportation demand is a highly variable process over space and time. A multinomial Probit (MNP) Model with spatially and temporally correlated error structure is proposed for freight demand analysis for tactical/operational planning applications. The resulting Model has a large number of alternatives, and estimation is performed using Monte-Carlo simulation to evaluate the MNP likelihoods. The Model is successfully applied to a data set of actual shipments served by a large truckload carrier. In addition to the substantive insights obtained from the estimation results, forecasting tests are performed to assess the Model's predictive ability for operational purposes.