理解区位基尼系数：局限性与基准分布的选择

 区位基尼系数虽然广泛用于度量产业的空间集聚,但很少有文献分析其应用的局限性和使用不同基准分布的影响。本文首先系统分析了区位基尼系数的三个主要缺陷：（1）受企业规模影响;（2）受地理单元设定的影响;（3）受产业分类影响。之后,本文分析了使用不同基准分布计算区位基尼系数所存在的经济意义上的差异,以及因此可能产生的理解偏差;同时,讨论了几种国内研究尚未使用过的比较有意义的基准分布形式。最后,本文以中国2004年制造业数据为例,解释了上述提出的问题。     

Calculation of Benchmark Doses from Continuous Data

A benchmark dose (BMD) is the dose of a substance that corresponds to a prescribed increase in the response (called the benchmark response or BMR) of a health effect. A statistical lower bound on the benchmark dose (BMDL) has been proposed as a replacement for the no-observed-adverse-effect-level (NOAEL) in setting acceptable human exposure levels. A method is developed in this paper for calculating BMDs and BMDLs from continuous data in a manner that is consistent with those calculated from quantal data. The method involves defining an abnormal response, either directly by specifying a cutoff x₀ that separates continuous responses into normal and abnormal categories, or indirectly by specifying the proportion P₀ of abnormal responses expected among unexposed subjects. The method does not involve actually dichotomizing individual continuous responses into quantal responses, and in certain cases can be applied to continuous data in summarized form (e.g., means and standard deviations of continuous responses among subjects in discrete dose groups). In addition to specifying the BMR and either x₀ or P₀ , the method requires specification of the distribution of continuous responses, including specification of the dose-response θ(d) for a measure of central tendency. A method is illustrated for selecting θ(d) to make the probability of an abnormal response any desired dose-response function. This enables the same dose-response model (Weibull, log-logistic, etc.) to be used for the probability of an abnormal response, regardless of whether the underlying data are continuous or quantal. Whenever the continuous responses are normally distributed with standard deviation σ (independent of dose), the method is equivalent to defining the BMD as the dose corresponding to a prescribed change in the mean response relative to σ.     

A Comparison of Three Methods for Calculating Confidence Intervals for the Benchmark Dose

Various methods exist to calculate confidence intervals for the benchmark dose in risk analysis. This study compares the performance of three such methods in fitting nonlinear dose-response models: the delta method, the likelihood-ratio method, and the bootstrap method. A data set from a developmental toxicity test with continuous, ordinal, and quantal dose-response data is used for the comparison of these methods. Nonlinear dose-response models, with various shapes, were fitted to these data. The results indicate that a few thousand runs are generally needed to get stable confidence limits when using the bootstrap method. Further, the bootstrap and the likelihood-ratio method were found to give fairly similar results. The delta method, however, resulted in some cases in different (usually narrower) intervals, and appears unreliable for nonlinear dose-response models. Since the bootstrap method is more time consuming than the likelihood-ratio method, the latter is more attractive for routine dose-response analysis. In the context of a probabilistic risk assessment the bootstrap method has the advantage that it directly links to Monte Carlo analysis.     

Quantitative Risk Assessment for Developmental Neurotoxic Effects

Developmental neurotoxicity concerns the adverse health effects of exogenous agents acting on neurodevelopment. Because human brain development is a delicate process involving many cellular events, the developing fetus is rather susceptible to compounds that can alter the structure and function of the brain. Today, there is clear evidence that early exposure to many neurotoxicants can severely damage the developing nervous system. Although in recent years, there has been much attention given to model development and risk assessment procedures for developmental toxicants, the area of developmental neurotoxicity has been largely ignored. Here, we consider the problem of risk estimation for developmental neurotoxicants from animal bioassay data. Since most responses from developmental neurotoxicity experiments are nonquantal in nature, an adverse health effect will be defined as a response that occurs with very small probability in unexposed animals. Using a two-stage hierarchical normal dose-response model, upper confidence limits on the excess risk due to a given level of added exposure are derived. Equivalently, the model is used to obtain lower confidence limits on dose for a small negligible level of risk. Our method is based on the asymptotic distribution of the likelihood ratio statistic (cf. Crump, 1995). An example is used to provide further illustration.     

Effects of Exposure Imprecision on Estimation of the Benchmark Dose

In regression analysis failure to adjust for imprecision in the exposure variable is likely to lead to underestimation of the exposure effect. However, the consequences of exposure error for determination of safe doses of toxic substances have so far not received much attention. The benchmark approach is one of the most widely used methods for development of exposure limits. An important advantage of this approach is that it can be applied to observational data. However, in this type of data, exposure markers are seldom measured without error. It is shown that, if the exposure error is ignored, then the benchmark approach produces results that are biased toward higher and less protective levels. It is therefore important to take exposure measurement error into account when calculating benchmark doses. Methods that allow this adjustment are described and illustrated in data from an epidemiological study on the health effects of prenatal mercury exposure.     

Benchmark Duration of Work Hours for Development of Fatigue Symptoms in Japanese Workers with Adjustment for Job‐Related Stress

The objective of this study was to calculate benchmark durations and lower 95% confidence limits for benchmark durations of working hours associated with subjective fatigue symptoms by applying the benchmark dose approach while adjusting for job‐related stress using multiple logistic regression analyses. A self‐administered questionnaire was completed by 3,069 male and 412 female daytime workers (age 18–67 years) in a Japanese steel company. The eight dependent variables in the Cumulative Fatigue Symptoms Index were decreased vitality, general fatigue, physical disorders, irritability, decreased willingness to work, anxiety, depressive feelings, and chronic tiredness. Independent variables were daily working hours, four subscales (job demand, job control, interpersonal relationship, and job suitability) of the Brief Job Stress Questionnaire, and other potential covariates. Using significant parameters for working hours and those for other covariates, the benchmark durations of working hours were calculated for the corresponding Index property. Benchmark response was set at 5% or 10%. Assuming a condition of worst job stress, the benchmark duration/lower 95% confidence limit for benchmark duration of working hours per day with a benchmark response of 5% or 10% were 10.0/9.4 or 11.7/10.7 (irritability) and 9.2/8.9 or 10.4/9.8 (chronic tiredness) in men and 8.9/8.4 or 9.8/8.9 (chronic tiredness) in women. The threshold amounts of working hours for fatigue symptoms under the worst job‐related stress were very close to the standard daily working hours in Japan. The results strongly suggest that special attention should be paid to employees whose working hours exceed threshold amounts based on individual levels of job‐related stress.     

Influence of Prenatal Mercury Exposure Upon Scholastic and Psychological Test Performance: Benchmark Analysis of a New Zealand Cohort

This paper presents benchmark (BMD) calculations and additional regression analyses of data from a study in which scores from 26 scholastic and psychological tests administered to 237 6- and 7-year-old New Zealand children were correlated with the mercury concentration in their mothers' hair during pregnancy. The original analyses of five test scores found an association between high prenatal mercury exposure and decreased test performance, using category variables for mercury exposure. Our regression analyses, which utilized the actual hair mercury level, did not find significant associations between mercury and children's test scores. However, this finding was highly influenced by a single child whose mother's mercury hair level (86 mgkg) was more than four times that of any other mother. When that child was omitted, results were more indicative of a mercury effect and scores on six tests were significantly associated with the mothers' hair mercury level. BMDs calculated from five tests ranged from 32 to 73 mgkg hair mercury, and corresponding BMDLs (95% lower limits on BMDs) ranged from 17 to 24 mgkg. When the child with the highest mercury level was omitted, BMDs ranged from 13 to 21 mgkg, and corresponding BMDLs ranged from 7.4 to 10 mgkg.     

A Simulation Study of Quantitative Risk Assessment for Bivariate Continuous Outcomes

The neurotoxic effects of chemical agents are often investigated in controlled studies on rodents, with binary and continuous multiple endpoints routinely collected. One goal is to conduct quantitative risk assessment to determine safe dose levels. Yu and Catalano (2005) describe a method for quantitative risk assessment for bivariate continuous outcomes by extending a univariate method of percentile regression. The model is likelihood based and allows for separate dose‐response models for each outcome while accounting for the bivariate correlation. The approach to benchmark dose (BMD) estimation is analogous to that for quantal data without having to specify arbitrary cutoff values. In this article, we evaluate the behavior of the BMD relative to background rates, sample size, level of bivariate correlation, dose‐response trend, and distributional assumptions. Using simulations, we explore the effects of these factors on the resulting BMD and BMDL distributions. In addition, we illustrate our method with data from a neurotoxicity study of parathion exposure in rats.     

社会工作实验室的建设原则与功能设置

社会工作实验室的建设在社会工作专业学生的培养中具有不可或缺的作用，但在建设的过程却缺乏统一的伦理标准与技术标准，对功能的设置也缺乏经验。针对这一问题，本文从社会工作实验室的建设原则与功能设置两方面进行了探讨与总结。     

大数据时代统计学重构研究中的几个热点问题

随着新兴科学技术的迅猛发展,我们目前正面临大数据时代。大数据是“未来的新兴石油”,它必将对未来的科技与社会经济的发展产生深远影响。认识大数据这个崭新的概念是一个逐步深化的过程,有必要将对其研究上升为国家意志,因为大数据将深刻影响人类的决策模式和社会经济的运行模式,将发展成为一个极具潜力新兴产业,最终成为解决中国社会经济、政治转型期的重要手段。 迄今为止,能借助的国内外文献不多,虽然国内很多研究工作者希望尽早进入相关的大数据研究领域,却无从下手。为此,本文前瞻性地介绍当今大数据统计学理论研究中的几个热点问题,以期能有更多的他人后续研究。