Bootstrap Estimation of Benchmark Doses and Confidence Limits with Clustered Quantal Data

The benchmark dose (BMD) is an exposure level that would induce a small risk increase (BMR level) above the background. The BMD approach to deriving a reference dose for risk assessment of noncancer effects is advantageous in that the estimate of BMD is not restricted to experimental doses and utilizes most available dose-response information. To quantify statistical uncertainty of a BMD estimate, we often calculate and report its lower confidence limit (i.e., BMDL), and may even consider it as a more conservative alternative to BMD itself. Computation of BMDL may involve normal confidence limits to BMD in conjunction with the delta method. Therefore, factors, such as small sample size and nonlinearity in model parameters, can affect the performance of the delta method BMDL, and alternative methods are useful. In this article, we propose a bootstrap method to estimate BMDL utilizing a scheme that consists of a resampling of residuals after model fitting and a one-step formula for parameter estimation. We illustrate the method with clustered binary data from developmental toxicity experiments. Our analysis shows that with moderately elevated dose-response data, the distribution of BMD estimator tends to be left-skewed and bootstrap BMDL s are smaller than the delta method BMDL s on average, hence quantifying risk more conservatively. Statistically, the bootstrap BMDL quantifies the uncertainty of the true BMD more honestly than the delta method BMDL as its coverage probability is closer to the nominal level than that of delta method BMDL. We find that BMD and BMDL estimates are generally insensitive to model choices provided that the models fit the data comparably well near the region of BMD. Our analysis also suggests that, in the presence of a significant and moderately strong dose-response relationship, the developmental toxicity experiments under the standard protocol support dose-response assessment at 5% BMR for BMD and 95% confidence level for BMDL.     

Mapping water quality by local scoring

The mean density of bacteria in a water body is commonly monitored using quantal assay. This paper describes the use of local scoring in estimating the spatial distribution of mean density from quantal assay results at a set of point locations. An application to estimating the mean density of fecal conform bacteria in a coastal pond is presented. Model diagnostics based on a parametric bootstrap are also presented.     

关于全着色Ramsey数

以 X_2(G)记一图 G 之全色数,全着色 Ramsey 数 X_2(m,n)为最小正整数 p,使得每一p 阶图 G 或有X_2(G)≥in加,或其补图■满足 X_2(■)≥n。本文给出 X_2(m,n)的上、下界。     

多元正态分布广义方差区间估计的改进

讨论了多元正态分布广义方差的区间估计问题,给出了在覆盖率及长度上均优于最优仿射同变区间估计的改进估计．     

初等r-循环矩阵的几个性质

给出了初等ｒ－循环矩阵可逆的充要条件及逆矩阵的表达式;对奇异的初等ｒ－循环矩阵给出了Ａ的一个ｇ－逆Ｇ（满足ＡＧＡ＝Ａ,ＧＡＧ＝Ｇ）及Ｍｏｏｒｅ－ｐｅｎｒｏｓｅ广义逆矩阵的表达式．     

Generalized Leverage and its Applications

The generalized leverage of an estimator is defined in regression models as a measure of the importance of individual observations. We derive a simple but powerful result, developing an explicit expression for leverage in a general M -estimation problem, of which the maximum likelihood problems are special cases. A variety of applications are considered, most notably to the exponential family non-linear models. The relationship between leverage and local influence is also discussed. Numerical examples are given to illustrate our results     

基于GRPLS 回归的 中国经济增长影响因素分析

构造一种新的方法———岭- 偏最小二乘回归方法(它既有效消除了因素变量之间的多 重共线性,又克服了传统方法的不足,且使模型更加稳健,具有更强的预测和分析能力) ;并运 用广义岭- 偏最小二乘回归方法分析了我国经济增长的影响因素,为我国制订持续、快速增长 的经济政策提供了有益的参考.     

Binary Dynamic Logit for Correlated Ordinal: estimation,application and simulation

We evaluate the estimation performance of the Binary Dynamic Logit model for correlated ordinal variables (BDLCO model), and compare it to GEE and Ordinal Logistic Regression performance in terms of bias and Mean Absolute Percentage Error (MAPE) via Monte Carlo simulation. Our results indicate that when the proportional-odds assumption does not hold, the proposed BDLCO method is superior to existing models in estimating correlated ordinal data. Moreover, this method is flexible in terms of modeling dependence and allows unequal slopes for each category, and can be used to estimate an apple bloom data set where the proportional-odds assumption is violated. We also provide a function in R to implement BDLCO.     

广义ATO下基于多种团购模型的供应链协调

针对由多个供应商、单个制造商和零售商所组成的广义按订单装配式供应链,在上游多个供应商不确定的零部件（或商品）供应以及下游客户不确定的需求的环境下,首先分析基本模型中团购前后的零售商和制造商的利润变化,然后提出了团购的标准模型和协调模型,并比较三种不同的团购模型对各成员以及全局供应链绩效的影响。通过数学推导证明和仿真数据算例分析,结果表明：零售商自发的团购基本模型总是对制造商有利,而对零售商的好处是有限的;由制造商主导的团购标准模型在满足一定条件时是优于团购基本模型的,但有使得零售商甚至制造商自身受到损失的可能;而集中决策下的团购协调模型能够提升全局供应链的期望利润,实现渠道的帕累托改善,但协调的效果会受到团购客户组成结构的限制。     

交易费用计量与广义价格

分析了新制度经济学家在交易费用计量研究上出现难题的原因,找到了一个可行的交易费用计量路径,指出了交易费用计量与广义价格的关系,提出了交易费用计量问题的解决办法和"交易费用的有机构成"概念。