A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo,Egypt

Abstract

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.     

Cash transfer program and child underweight—Empirical evidence from a causal mediation analysis

Despite the existence of a voluminous literature on cash transfer programs, little is known about their impacts on the underweight of children. To fill the knowledge gap, this study uses a unique individual panel data set to analyze how participation in the Children Sponsorship Program (CSP) improves underweight status among needy children and adolescents in Taiwan. This study examines not only the program effect on children's underweight, but also underscores the potential pathways behind the program effect by employing a causal mediation analysis. Our analysis finds that exiting the CSP has a negative impact on the improvement of underweight status. Moreover, eating breakfast every day and receiving pocket money from parents can be two significant mediators that link the effect of exiting the CSP and the change in underweight status among children and adolescents.     

Venture capital,innovation, and growth: evidence from Chinese metropolitan data

Using panel data from 282 Chinese cities from 2003 to 2015, we estimate the impact of venture capital (VC) on innovation, employment, and payroll in metropolitan areas. Controlling for endogeneity, VC investments are significantly positively related to metropolitan innovation, employment, and payroll in a fixed effects panel regression and dynamic GMM estimation. Using the number of IPOs (Initial public offerings) in the city as an instrumental variable to again control for endogeneity, VC remains significant. Our results show that VC is significantly contributing to the metropolitan economy as a whole by supporting innovation, creating jobs, and generating wealth in the local cities.     

Nonparametric dynamic panel data models: Kernel estimation and specification testing

Motivated by the first-differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogenous regressors. The estimators utilize the additive structure of the first-differenced model—the fact that the two additive components have the same functional form, and the unknown function of interest is implicitly defined as a solution of a Fredholm integral equation of the second kind. We establish the uniform consistency and asymptotic normality of the estimators. We also propose a consistent test for the correct specification of linearity in typical dynamic panel data models based on the _L2$L_2$ distance of our nonparametric estimates and the parametric estimates under the linear restriction. We derive the asymptotic distributions of the test statistic under the null hypothesis and a sequence of Pitman local alternatives, and prove its consistency against global alternatives. Simulations suggest that the proposed estimators and tests perform well for finite samples. We apply our new method to study the relationships among economic growth, the initial economic condition and capital accumulation, and find a significant nonlinear relation between economic growth and the initial economic condition.     

系统思考理论在部队装备管理信息化建设中的应用

系统思考是一种有效地解决复杂性问题的崭新思维模式和科学方法。部队装备管理信息化建设是当前我军装备管理领域着力进行的一项系统工程,以系统思考理论为指导,对于科学认识和指导部队装备管理信息化建设具有重要意义。从系统思考的基本理论着手,分析了系统思考在部队装备管理信息化建设中的重要作用,并在系统思考理论的指导下提出了有助于部队装备管理信息化建设的建议,为科学推动部队装备管理信息化建设提供参考。     

三相交流电动机智能测控系统的研究

根据三相交流电动机的性能和常见故障,运用虚拟仪器、串口通讯等技术通过对电动机电参量信号的分析提出对三相交流电动机运行状态的测控与保护的方法,构建三相交流电动机智能测控系统,使三相交流电动机的测控与保护进入图形化、数字化、网络化、自动化、远程遥控的智能化实时测控时代。     

Sieve estimation of panel data models with cross section dependence

In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006) common correlated effect (CCE) estimator to our semiparametric framework. Asymptotic normal distributions for the proposed estimators are derived and asymptotic variance estimators are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.     

广东省对外贸易与经济增长关系的实证分析

文章采用现代计量经济学的分析方法--协整分析和格兰杰因果关系检验,对广东省改革开放以来对外贸易与经济增长的相互关系进行实证分析,结论表明:广东省对外贸易与经济增长之间存在着相互促进的关系,其中,广东省GDP增长对进、出口增加的促进作用大于进、出口增加对GDP增长的促进作用.     

A consistent characteristic function-based test for conditional independence

Y is conditionally independent of Z given X   if Pr{f(y|X,Z)=f(y|X)}=1$\{f(y|X,Z)=f(y|X)\}=1$ for all y on its support, where f(·|·)$f(·|·)$ denotes the conditional density of Y   given (X,Z)$(X,Z)$ or X.$X.$ This paper proposes a nonparametric test of conditional independence based on the notion that two conditional distributions are equal if and only if the corresponding conditional characteristic functions are equal. We extend the test of Su and White (2005. A Hellinger-metric nonparametric test for conditional independence. Discussion Paper, Department of Economics, UCSD) in two directions: (1) our test is less sensitive to the choice of bandwidth sequences; (2) our test has power against deviations on the full support of the density of (X,Y,Z$X,Y,Z$). We establish asymptotic normality for our test statistic under weak data dependence conditions. Simulation results suggest that the test is well behaved in finite samples. Applications to stock market data indicate that our test can reveal some interesting nonlinear dependence that a traditional linear Granger causality test fails to detect.     

三峡库区万州某中学高边坡支护方案价值分析

利用价值工程原理对三峡库区万州某中学新校址高边坡的加固方案进行评估。评估比较发现,在满足稳定性要求的情况下,锚索抗滑桩方案的价值要优于锚索格构方案的价值。同时利用价值工程原理对所选用方案的各分项工程的功能进行优化,使功能与成本更趋合理。最大限度的满足了功能的需求度,降低了成本,创造了较好的经济效益。