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A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates

Tianjian Yu, Fan Gao, Xinyuan Liu, Jinjun Tang
2021 Sensors  
Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution  ...  To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling  ...  how the regional factors influenced the crash rates changed with the number of crash rates using a spatial autoregressive quantile (SARQ) model.  ... 
doi:10.3390/s22010005 pmid:35009547 pmcid:PMC8747712 fatcat:4zubucsiqbhjna4dum7uc53zqy

Crash rates analysis in China using a spatial panel model

Wonmongo Lacina Soro, Yiwei Zhou, Didier Wayoro
2017 IATSS Research  
Yet, the quasi-totality of spatial dependence studies on crash rates is performed within the framework of single-equation spatial cross-sectional studies.  ...  The spatial and error lags are all positive and statistically significant suggesting similarities of crash rates pattern in neighboring regions.  ...  Acknowledgments The authors are very thankful to the anonymous reviewers for their invaluable comments and suggestions to improve the content and presentation of the manuscript.  ... 
doi:10.1016/j.iatssr.2016.11.001 fatcat:tnwaamt2yvdlpkk75zedpe43b4

Economic Downturn, Change in Unemployment, and the Midwest: A Quantile Regression Approach

Kathleen G. Arano, Arun K. Srinivasan
2022 The Review of Regional Studies  
We use a quantile regression approach to examine the tails of the distribution of change in unemployment rates between 2006 and 2009 across all counties in the Midwest.  ...  Likewise, local labor mobility has amplifying effects on the change in unemployment rates, while educational attainment has a moderating effect.  ...  , i.e., spatial quantile regression.  ... 
doi:10.52324/001c.34680 fatcat:rp3tn4kfbbdfpowdcw7z34c66u

Environmental and historical constraints on global patterns of amphibian richness

L. B Buckley, W. Jetz
2007 Proceedings of the Royal Society of London. Biological Sciences  
We present a global analysis of contemporary environmental and historical constraints on amphibian richness, the first for an ectotherm clade at this scale.  ...  Amphibians are presumed to experience environmental constraints distinct from those of better studied endothermic taxa due to their stringent water requirements and the temperature dependence of their  ...  Bivariate plots of environmental effects on richness across 40 315 equal area quadrats equivalent to 0.58 size covering the world except islands showing 10, 50 and 90% quantile regressions.  ... 
doi:10.1098/rspb.2006.0436 pmid:17327208 pmcid:PMC2189569 fatcat:qg5lgidt3vhpdg43eewkkchbs4

Exploring spatio-temporal effects in traffic crash trend analysis

Chenhui Liu, Anuj Sharma
2017 Analytic Methods in Accident Research  
In this study, we used Bayesian spatio-temporal models to investigate regional crash frequency trends, and explored the effects of omitting spatial or temporal trends in spatio-temporal correlated data  ...  Although many studies have included either spatial or temporal effects in crash frequency modeling, only a limited number of studies have considered both.  ...  No explanatory factors, except VMT, were found to have a significant influence on fatal crash frequencies.  ... 
doi:10.1016/j.amar.2017.09.002 fatcat:w7ywviulefcnvafd4jhneeh4ay

Migration in Spain: The Role of Cultural Diversity Revisited

Maite Alguacil, Luisa Alamá-Sabater
2021 Politics and Governance  
To do that, we use panel data techniques that treat cultural diversity as an endogenous variable and account for spatial linkages.  ...  The dual nature of immigrants in Spain, that is, working and retired migration, is also considered in our regressions.  ...  Acknowledgments Authors would like to thank the Universitat Jaume I (UJI-B2020-57) for their financial support.  ... 
doi:10.17645/pag.v9i4.4458 fatcat:iyeipot4c5fhphdfb5rew625hy

Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression

Xinyu Qu, Xinyan Zhu, Xiongwu Xiao, Huayi Wu, Bingxuan Guo, Deren Li
2021 ISPRS International Journal of Geo-Information  
Some studies on the impact of traditional land use factors on traffic crashes do not take into account the limitations of spatial heterogeneity and spatial scale.  ...  of different influencing factors, to explore the influence of reclassified points-of-interest (POI) on traffic crashes occurring on weekdays and weekends.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi10110791 fatcat:bp5vbiugzbg7xlw6ovkigjzky4

Practical Minimum Sample Size for Road Crash Time-Series Prediction Models

Fady M. A. Hassouna, Khaled Al-Sahili, Valeria Vignali
2020 Advances in Civil Engineering  
In this study, the effect of sample size (number of years used to develop a prediction model) on the crash prediction accuracy using Autoregressive integrated moving average (ARIMA) method was investigated  ...  Based on the availability of annual crash records, road crash data for four selected countries (Denmark, Turkey, Germany, and Israel) were used to develop the crash prediction models based on different  ...  rate, length, and vertical grade are important variables in explaining the severity of crashes. e negative binomial regression model was used to predict injury frequency and fatality per year on Advances  ... 
doi:10.1155/2020/6672612 fatcat:zspo4r3buvflrkltpfbh2ouqoi

Smoothly mixing regressions

John Geweke, Michael Keane
2007 Journal of Econometrics  
The first illustration studies quantiles of the distribution of earnings of men conditional on age and education, and shows that smoothly mixing regressions are an attractive alternative to non-Baeysian  ...  quantile regression.  ...  There is also evidence of a leverage effect, that is, greater volatility following a negative return than a positive one.  ... 
doi:10.1016/j.jeconom.2006.05.022 fatcat:64vueg6t3bgqfdynmx3dy26skm

Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition

Cristina Mollica, Lea Petrella
2016 Journal of Applied Statistics  
The working group (WG) CMStatistics comprises a number of specialized teams in various research areas of computational and methodological statistics.  ...  The teams act autonomously within the framework of the WG in order to promote their own research agenda. Their activities are endorsed by the WG.  ...  Further assume that various environmental factors, common to the regions trigger the recurrence of the event of interest leading to spatially correlated recurrent event data.  ... 
doi:10.1080/02664763.2016.1263835 fatcat:l5eyielgxrct7hq5ljqeej5ccy

An empirical link between the spectral colour of climate and the spectral colour of field populations in the context of climate change

Bernardo García-Carreras, Daniel C. Reuman
2011 Journal of Animal Ecology  
Climate and population phenomena have a spatial structure, however, that needs to be accounted for to avoid inflation of Type I error rates (Legendre & Legendre 1998).  ...  For mean summer temperatures, Asia an ustralasia became redder. and other regions became (Fig. 2) Distributions of the spectral exponents of climate variables appeared symmetric and unimodal, quantile-quantile  ... 
doi:10.1111/j.1365-2656.2011.01833.x pmid:21466552 fatcat:bnqsfqvfqfd7znehtu4qeuptsa

Panel Data Methods and Applications to Health Economics [chapter]

Andrew M. Jones
2009 Palgrave Handbook of Econometrics  
variation to identify the treatment effects of interest.  ...  Models for longitudinal data 5.1 Applications of linear models 5.2 Applications with categorical outcomes 5.3 Applications with count data 5.4 Applications of quantile regression and other semiparametric  ...  They compare specifications that allow for a spatial autoregressive process in the error term; a random effects model with spatially lagged dependent variables; and a random effects model with spatial  ... 
doi:10.1057/9780230244405_12 fatcat:4k3cr7zqwnegrdxaqm226mwj54

Spatial Statistical Models: An Overview under the Bayesian Approach

Francisco Louzada, Diego Carvalho do Nascimento, Osafu Augustine Egbon
2021 Axioms  
However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of  ...  Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood.  ...  spatial quantile regression model with an asymmetric Laplace spatial component to determine the risk factors of the radon-222 noble gas, which arises naturally from uranium decays; Reference [72] adopted  ... 
doi:10.3390/axioms10040307 fatcat:a6nnkw3imjgcjh4hd6ttkr4ln4

Population dynamics of the threatened Cumberland Sound beluga (Delphinapterus leucas) population

Cortney Watt, Marianne Marcoux, Steven H. Ferguson, Mike Hammill, Cory Matthews
2020 Arctic Science  
Engaged co-management of the Cumberland Sound beluga population and information on demographic parameters, such as reproductive rates, and age and sex composition of the harvest, are needed to restore  ...  Long-term trends in abundance were examined by fitting a Bayesian surplus-production population model to a time series of abundance estimates (n = 5), flown between 1990 and 2017, taking into account reported  ...  at Washington University, and the Department of Government and the Institute for Quantitative Social Science at Harvard University.  ... 
doi:10.1139/as-2019-0030 fatcat:pt6rtraxingg3fmrqnei3obnty

Fully distribution-free center-outward rank tests for multiple-output regression and MANOVA [article]

Marc Hallin, Daniel Hlubinka, Šárka Hudecová
2021 arXiv   pre-print
Extending rank-based inference to a multivariate setting such as multiple-output regression or MANOVA with unspecified d-dimensional error density has remained an open problem for more than half a century  ...  A concept of center-outward multivariate ranks and signs based on measure transportation ideas has been introduced recently.  ...  That grid Gn is obtained as follows: (a) first factorize n into n = nR nS + n0 , with 0 ≤ n0 < min(nR , nS ); (b) next consider a “regular array” SnS := {sn1 S , . . . , snnSS } of nS points on the  ... 
arXiv:2007.15496v2 fatcat:nafwmteth5f2fbmh5ntyya54hq
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