2019—2020年长沙市生产安全事故统计分析

为探究生产安全事故的演化规律，基于长沙市2019—2020年生产安全事故的数据，从事故发生时间、发生区域、行业和领域分布、事故等级等方面进行分析。研究表明：2019—2020年事故数量和死亡人数呈下降趋势，1月、8月、10月事故数量和死亡人数较多，2月事故数量和死亡人数少；非主城区的事故数量和死亡人数高于主城区，事故数量和死亡人数在空间上的分布比较均衡；道路运输和工矿商贸行业在全市生产安全事故中占绝大多数；工矿商贸行业中，建筑施工行业的事故数量和死亡人数最多；一般事故所产生的事故数量和死亡人数最多，重大事故的事故死亡率最高。     

Concentration behavior of the penalized least squares estimator

Consider the standard nonparametric regression model and take as estimator the penalized least squares function. In this article, we study the trade‐off between closeness to the true function and complexity penalization of the estimator, where complexity is described by a seminorm on a class of functions. First, we present an exponential concentration inequality revealing the concentration behavior of the trade‐off of the penalized least squares estimator around a nonrandom quantity, where such quantity depends on the problem under consideration. Then, under some conditions and for the proper choice of the tuning parameter, we obtain bounds for this nonrandom quantity. We illustrate our results with some examples that include the smoothing splines estimator.     

Inference for large financial systems

We treat the parameter estimation problem for mean‐field models of large interacting financial systems such as the banking system and a pool of assets held by an institution or backing a security. We develop an asymptotic inference approach that addresses the scale and complexity of such systems. Harnessing the weak convergence results developed for mean‐field financial systems in the literature, we construct an approximate likelihood for large systems. The approximate likelihood has a conditionally Gaussian structure, enabling us to design an efficient numerical method for its evaluation. We provide a representation of the corresponding approximate estimator in terms of a weighted least‐squares estimator, and use it to analyze the large‐system and large‐sample behavior of the estimator. Numerical results for a mean‐field model of systemic financial risk highlight the efficiency and accuracy of our estimator.     

GRADING JOURNALS IN ECONOMICS: THE ABCS OF THE ABDC

Many institutions and governments grade academic journals for the evaluation of research. In this paper, we implement a multi-bibliometric methodology for the evaluation of such a list of journal grades. We examine the grades assigned by the Australian Business Deans Council (ABDC) for over 750 journals in the fields of economics and statistics. Firstly, we generate up to 48 bibliometric-based grades for each journal based on the grade distribution implied by the ABDC. Secondly, we categorize the bibliometrics employing a cluster analysis of an interrater agreement statistic. Thirdly, we present a visualization of the consistency of the grading by journal. Finally, we list those journals where the majority of the matched bibliometrics indicate a higher or lower grade than their ABDC grade.     

Estimating Discrete Joint Probability Distributions for Demographic Characteristics at the Store Level Given Store Level Marginal Distributions and a City-Wide Joint Distribution

This paper provides a solution to the problem of estimating a joint distribution using the associated marginal distributions and a related joint distribution. The particular application we have in mind is estimating joint distributions of demographic characteristics corresponding to market areas for individual retail stores. Marginal distributions are generally available at the census tract level, but joint distributions are only available for Metropolitan Statistical Areas which are generally much larger than the market for a single retail store. Joint distributions over demographics are an important input into mixed logit demand models for aggregate data. Market shares that vary systematically with demographics are essential for relieving the restrictions imposed by the Independence from Irrelevant Alternative property of the logit model.We approach this problem by formulating a parametric function that incorporates both the city-wide joint distributional information and marginal information specific to the retail stores market area. To estimate the function, we form moment conditions equating the moments of the parametric function to observed data, and we input these into a GMM objective. In one of our illustrations we use four marginal demographic distributions from each of eight stores in Dominicks Finer Foods data archive to estimate a four dimensional joint distribution for each store. Our results show that our GMM approach produces estimated joint distributions that differ substantially from the product of marginal distributions and emit marginals that closely match the observed marginal distributions. Mixed logit demand estimates are also presented which show the estimates to be sensitive to the formulation of the demographics distribution.The views expressed are not purported to reflect those of the United States Department of Justice     

公共自行车服务系统的分析及改进

本文对温州市鹿城区公共自行车服务系统180个站点进行调查,综合分析研究了每个站点的借还车状况和用车时长,进一步结合温州市鹿城区地图,定义两站点之间的距离,然后综合考虑各站点的锁桩数量对公共自行车服务系统进行评价,最后为公共自行车交通服务系统提供一些解决方案,以便更好的完善公共自行车租用系统。     

Applications of Statistics in the Field of General Insurance: An Overview

This is an expository paper on applications of statistics in the field of general insurance, also called non‐life insurance. Unlike life insurance where advanced statistical techniques have long been part of financial mathematics and actuarial applications, their use is only relatively recent in non‐life insurance. The business model of insurance companies, especially those active in non‐life insurance, has seen dramatic changes over the last 15 years. The aim of this paper is to convince the readers that especially today non‐life insurance is not only an exciting ground to apply existing modern statistical tools but also a fertile environment for new and challenging statistical developments. The activities of an insurance company can be viewed as an industrial process where data management and data analysis play a key role. That is why a fundamental understanding of data‐related issues (such as data quality, variability, analysis and correct interpretation) is so essential to the insurance business. These are exactly the tasks where professional statisticians excel. Also, a better understanding of the field of general insurance by statisticians will promote fruitful exchanges between actuaries and statisticians, thereby helping to bring actuarial and statistical professional societies closer to each other. Selected examples are used to cover the essential aspects of general insurance, and all of them are based on the author's experience. The paper concludes with some remarks on the role of statisticians working in general insurance.     

Modeling and Measuring Economic Reality: Reply to the Reviews

To begin with, we'd like to express our appreciation to our three commentators for their thoughtful and helpful reviews. Like the founders of the subject, we believe, and our reviewers seem to agree, that structural econometrics has the potential to enable us to develop serious working models of how different economies actually operate, and also to tell us something about the changing patterns of their growth and transformation. But both we and our reviewers agree that there is a great deal wrong with the way econometrics is practiced today.     

探索性因子分析中统计方法的比对研究

本文以探索性因子分析中统计方法的选择为目标,针对在许多应用探索性因子分析的文献中存在的统计方法选择不当致使计算结果失真这一现象,对探索性因子分析中的15种统计方法进行了比对研究,并给出了建议。     

Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks

Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. ( 2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of “new age” classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that “new age” classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).