Estimation of the marginal expected shortfall under asymptotic independence

We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so-called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, which is shown to be asymptotically normal. The finite sample performance of the estimator is investigated in a small simulation study. The method is also applied to estimate the expected amount of rainfall at a weather station given that there is a once every 100 years rainfall at another weather station nearby.     

Book Reviews

Books reviewed:
M Hollander and D Wolfe, Nonparametric Statistical Methods
T Leonard and J.S.J Hsu, Bayesian Methods     

Measuring agreement between two statistics with applications to age standardization

Summary.  We detail a general method for measuring agreement between two statistics. An application is two ratios of directly standardized rates which differ only by the choice of the standard. If the statistics have a high value for the coefficient of agreement then the expected squared difference between the statistics is small relative to the variance of the average of the two statistics, and inferences vary little by changing statistics. The estimation of a coefficient of agreement between two statistics is not straightforward because there is only one pair of observed values, each statistic calculated from the data. We introduce estimators of the coefficient of agreement for two statistics and discuss their use, especially as applied to functions of standardized rates.     

林业投资均衡性的理论分析

我国林业投资体制的改革面临着许多新的课题,研究市场经济条件下林业投资状况十分必要。首先,从均衡理论出发,在分析宏观经济政策对林业的影响和林业投资现状基础上,对形成这种状况的主要原因进行了探讨;其次,以林业投资对象为主体,对其微观经济行为的成本与收益问题进行了分析;最后,提出深化林业投资体制改革要促进投资主体多元化,给予林业更多的政策扶持,进一步完善林业法律法规,以促进我国林业持续稳定地发展。     

Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.     

社会边缘群体对社会稳定的影响及对策--以哈尔滨建筑业农民工为例

社会稳定和社会边缘群体的生存状况紧密相连.建筑业农民工群体是中国社会边缘群体的重要组成部分,必须通过实证研究分析他们的生存状态及其与社会稳定之间的关系.通过抽样方法和问卷调查,从经济状况、社会分配、社会心理和社会控制等四个方面透视哈尔滨市建筑业农民工群体的生活状况,目前还不是影响社会稳定的因素.但从长期来看,若不注重研究并改善其境遇,则可能成为社会稳定的隐患.鉴于此,必须综合利用农民工社会保障制度、工会组织、技能培训、改善食宿和安全条件以及多样化的休闲途径,来保证这一群体的生活质量和合法权益,以维护社会稳定.     

反Fuzzy子群的直积

1990年R.Biswas在[2]中提出了反Fuzzy子群的概念.在此基础上本文相应定义了反Fuzzy子群的直积.接着讨论了其包括正规、共轭情况下的若干性质.此外,还给出了直积μ_1×μ_2是G×G上的反Fuzzy子群的一个充要条件.     

SHOULD WE ESTIMATE THE PROBABILITY OF CORRECT SELECTION?

Various authors, given k location parameters, have considered lower confidence bounds on (standardized) dserences between the largest and each of the other k - 1 parameters. They have then used these bounds to put lower confidence bounds on the probability of correct selection (PCS) in the same experiment (as was used for finding the lower bounds on differences). It is pointed out that this is an inappropriate inference procedure. Moreover, if the PCS refers to some later experiment it is shown that if a non-trivial confidence bound is possible then it is already possible to conclude, with greater confidence, that correct selection has occurred in the first experiment. The short answer to the question in the title is therefore ‘No’, but this should be qualified in the case of a Bayesian analysis.     

Optimal scaling of Metropolis algorithms: Heading toward general target distributions

The authors provide an overview of optimal scaling results for the Metropolis algorithm with Gaussian proposal distribution. They address in more depth the case of high‐dimensional target distributions formed of independent, but not identically distributed components. They attempt to give an intuitive explanation as to why the well‐known optimal acceptance rate of 0.234 is not always suitable. They show how to find the asymptotically optimal acceptance rate when needed, and they explain why it is sometimes necessary to turn to inhomogeneous proposal distributions. Their results are illustrated with a simple example.     

Uncertain Numbers and Uncertainty in the Selection of Input Distributions—Consequences for a Probabilistic Risk Assessment of Contaminated Land

Risks from exposure to contaminated land are often assessed with the aid of mathematical models. The current probabilistic approach is a considerable improvement on previous deterministic risk assessment practices, in that it attempts to characterize uncertainty and variability. However, some inputs continue to be assigned as precise numbers, while others are characterized as precise probability distributions. Such precision is hard to justify, and we show in this article how rounding errors and distribution assumptions can affect an exposure assessment. The outcome of traditional deterministic point estimates and Monte Carlo simulations were compared to probability bounds analyses. Assigning all scalars as imprecise numbers (intervals prescribed by significant digits) added uncertainty to the deterministic point estimate of about one order of magnitude. Similarly, representing probability distributions as probability boxes added several orders of magnitude to the uncertainty of the probabilistic estimate. This indicates that the size of the uncertainty in such assessments is actually much greater than currently reported. The article suggests that full disclosure of the uncertainty may facilitate decision making in opening up a negotiation window. In the risk analysis process, it is also an ethical obligation to clarify the boundary between the scientific and social domains.