Nonparametric Benchmark Dose Estimation with Continuous Dose‐Response Data

We propose a new method for risk‐analytic benchmark dose (BMD) estimation in a dose‐response setting when the responses are measured on a continuous scale. For each dose level d, the observation X(d) is assumed to follow a normal distribution: . No specific parametric form is imposed upon the mean μ(d), however. Instead, nonparametric maximum likelihood estimates of μ(d) and σ are obtained under a monotonicity constraint on μ(d). For purposes of quantitative risk assessment, a ‘hybrid’ form of risk function is defined for any dose d as R(d) = P[X(d) < c], where c > 0 is a constant independent of d. The BMD is then determined by inverting the additional risk functionR_A(d) = R(d) ? R(0) at some specified value of benchmark response. Asymptotic theory for the point estimators is derived, and a finite‐sample study is conducted, using both real and simulated data. When a large number of doses are available, we propose an adaptive grouping method for estimating the BMD, which is shown to have optimal mean integrated squared error under appropriate designs.     

A modified economic-statistical design of the T2 control chart with variable sample sizes and control limits

Recent studies have shown that using variable sampling size and control limits (VSSC) schemes result in charts with more statistical power than variable sampling size (VSS) when detecting small to moderate shifts in the process mean vector. This paper presents an economic-statistical design (ESD) of the VSSC T² control chart using the general model of Lorenzen and Vance [22]. The genetic algorithm approach is then employed to search for the optimal values of the six test parameters of the chart. We then compare the expected cost per unit of time of the optimally designed VSSC chart with optimally designed VSS and FRS (fixed ratio sampling) T² charts as well as MEWMA charts.     

A distribution-free multivariate CUSUM control chart using dynamic control limits

In modern quality control, it is becoming common to simultaneously monitor several quality characteristics of a process with rapid evolving data-acquisition technology. When the multivariate process distribution is unknown and only a set of in-control data is available, the bootstrap technique can be used to adjust the constant limit of the multivariate cumulative sum (MCUSUM) control chart. To further improve the performance of the control chart, we extend the constant control limit to a sequence of dynamic control limits which are determined by the conditional distribution of the charting statistics given the sprint length. Simulation results show that the novel control chart with dynamic control limits offers a better ARL performance, compared with the traditional MCUSUM control chart. Despite it, the proposed control chart is considerably computer-intensive. This leads to the development of a more flexible control chart which uses a continuous function of the sprint length as the control limit sequences. More importantly, the control chart is easy to implement and can reduce the computational time significantly. A white wine data illustrates that the novel control chart performs quite well in applications.     

Computing exact one-sided confidence limits for treatment effect in clinical trials

One standard summary of a clinical trial is a confidence limit for the effect of the treatment. Unfortunately, standard approximate limits may have poor frequentist properties, even for quite large sample sizes. It has been known since Buehler (1957 Buehler, R. J. (1957). Confidence intervals for the product of two binomial parameters. Journal of Computational and Graphical Statistics 52:482–493. [Google Scholar]) that an imperfect confidence limit can be adjusted to have exact coverage. These “tight” limits are the gold standard frequentist confidence limit. Computing tight limits requires exact calculation of certain tail probabilities and optimisation of potentially erratic functions of the nuisance parameter. Naive implementation is both computationally unreliable and highly burdensome, and perhaps explains why they are not in common use. For clinical trials however, where the data and parameter have dimension two, the difficulties can be fully surmounted. This paper brings together several results in the area and applies them to simple two dimensional problems. It is shown how to reduce the computational burden by an order of magnitude. Difficulties with the optimisation reliability are mitigated by applying two different computational strategies, which tend to break down under different conditions, and taking the less stringent of the two computed limits. This paper specifically develops limits for the relative risk in a clinical trial, but it should be clear to the reader that the method extends to arbitrary measures of treatment effect without essential modification.     

可译性与不可译性——一个语言学的视角

可译性与不可译性问题是当代西方翻译理论的核心问题之一,也是翻译界一个争论已久的问题。本文在基于语言是可译的这个前提之下,借助当代语言学的研究成果,对可译性与不可译性问题在理论上作了一个较为充分的论述与总结,从而揭示可译性和不可译性之间的辩证关系。     

Confidence limits and prediction limits for a Weibull distribution based on the generalized variable approach

In this article, we consider the problems of constructing confidence interval for a Weibull mean and setting prediction limits for future samples. Specifically, we construct upper prediction limits that include at least l$l$ of m$m$ samples from a Weibull distribution at each of r$r$ locations. The methods are based on the concept of generalized variable approach. The procedures can be easily extended to the type II censored samples, and they can be used to find approximate inferential procedures for type I censored samples. The proposed methods are conceptually simple and easy to use. The results are illustrated using some practical examples.     

民俗文化词语的可译性限度及其翻译策略

民俗词语具有丰富的文化内涵.在跨文化交际活动中,民俗文化词语的翻译起着难以替代的作用.本文从生态文化、器物文化、宗教文化、社会文化以及语言文化方面阐述了民俗文化词语的可译性限度并提出了一些补偿策略.     

Adjusting Cornish–Fisher expansions and confidence intervals for the effect of roundoff

Suppose we want to estimate some smooth function of two types of parameters. The first can be estimated by sample means, while the second is known exactly up to the number of decimal places recorded, that is they are subject to roundoff. We obtain the Cornish–Fisher expansions and associated nonparametric confidence intervals for such functions. These results are illustrated by a simulation study.     

Some Observations on the Correlation Determinant

The lower 5% point of the correlation determinant in the null case, that is with zero parental correlations in the multivariate, normally distributed, data set, are presented for sample sizes up to 30. Repeated Monte Carlo simulation suggests that the limits are correct to +/?2 units in the third place of decimals. Thus the limits permit a test of hypothesis for the mutual independence of the variates involved. For sample sizes greater than 30, an asymptotic approximation based on the chi-squared distribution, as proposed by Morrison (2005 Morrison , D. F. ( 2005 ). Multivariate Statistical Methods , 4th . ed. London : Thompson Brooks/Cde . [Google Scholar]), is shown to be quite reliable.     

Tables of confidence limits on the tail area of the normal distribution

Tables are given of confidence limits on tail areas, γ, of the normal distribution, where γ = P{Y ≥ L}, and where L is a given number, and Y is normally distributed with unknown mean, μ, and unknown variance, σ².