Optimal spacings for the simultaneous estimation of the location and scale parameters of a normal distribution based on selected two sample quantiles

Simultaneous estimation of the location parameter μ and scale parameter σ of a normal distribution based on two selected sample quantiles out of sufficiently large sample of size n is considered. The optimal spacing which maximizes the asymptotic relative efficiency is proved to be symmetric.     

语文教材中的“例文”及其编撰策略

"例文"的观念是在对传统语文教材的批判中形成的。"例文"的实质是将本来含有无限可能性的诗文,限制在一个特定的侧面、特定的点来作为"例子"。欧美的"文学"教育倾向采用"分析结构",语文课程知识含量丰富,这些知识往往通过"例文"这一途径、手段来进行教学,其"例文"编撰策略比较成熟。相比较,我国语文教材的编撰策略和技术仍属落后。     

中国证券市场星期效应逐渐消失的经验证据

本文研究证实了中国证券市场在早期具有明显的星期五效应,而在星期一和星期二的收益普遍较低。结算制度和信息披露制度是造成我国股市早期星期效应的主要原因,在T 1交易制度下,星期五的高收益率是要补偿两天的资金成本和风险成本;而信息披露制度的变革导致星期效应的不同表现。然而,滚动样本检验和分年度检验发现从1997年开始中国证券市场星期效应消失。这说明了中国证券市场有效性在不断提高。     

Likelihood-Based Inference for the Shape Parameter of a Two Parameter Weibull Distribution

Based on a Type 2 censored sample, we use the likelihood-based approach to draw likelihood inference on the shape parameter gamma of a two-parameter Weibull distribution. In particular, we derive the profile, conditional and marginal likelihoods of gamma. Numerical results along with some concluding remarks regarding the use of likelihood-based methods for inference are provided.     

学术期刊数字化新特征及发展对策

牵引变压器是动车组电力牵引系统的重要设备,牵引变压器工作状态是否正常直接关系到动车组的安全运行。随着动车组使用率的日益提高,牵引变压器的检查和维修也越来越引起关注。通常采用定期试验、定期维护的检修方法,但这些方法存在过维护或欠维护等问题。通过分析统计数据对变压器进行状态检修是解决此类问题的有效方法。针对统计数据的分析,数理统计学中u检验方法广泛应用于医药学、食品、体育测量等多个领域。提出利用数理统计中的u检验方法对动车组的变压器实施故障检测,并给出实验数据和计算结果,以期能够在实际应用中达到状态检修的目的     

A novel test to compare two treatments based on endpoints involving both nonfatal and fatal events

In a clinical trial comparing two treatment groups, one commonly‐used endpoint is time to death. Another is time until the first nonfatal event (if there is one) or until death (if not). Both endpoints have drawbacks. The wrong choice may adversely affect the value of the study by impairing power if deaths are too few (with the first endpoint) or by lessening the role of mortality if not (with the second endpoint). We propose a compromise that provides a simple test based on the time to death if the patient has died or time since randomization augmented by an increment otherwise. The test applies the ordinary two‐sample Wilcoxon statistic to these values. The formula for the increment (the same for experimental and control patients) must be specified before the trial starts. In the simplest (and perhaps most useful) case, the increment assumes only two values, according to whether or not the (surviving) patient had a nonfatal event. More generally, the increment depends on the time of the first nonfatal event, if any, and the time since randomization. The test has correct Type I error even though it does not handle censoring in a customary way. For conditions where investigators would face no easy (advance) choice between the two older tests, simulation results favor the new test. An example using a renal‐cancer trial is presented. Copyright © 2015 John Wiley & Sons, Ltd.     

Testing hypotheses about medical test accuracy: considerations for design and inference

Developing new medical tests and identifying single biomarkers or panels of biomarkers with superior accuracy over existing classifiers promotes lifelong health of individuals and populations. Before a medical test can be routinely used in clinical practice, its accuracy within diseased and non-diseased populations must be rigorously evaluated. We introduce a method for sample size determination for studies designed to test hypotheses about medical test or biomarker sensitivity and specificity. We show how a sample size can be determined to guard against making type I and/or type II errors by calculating Bayes factors from multiple data sets simulated under null and/or alternative models. The approach can be implemented across a variety of study designs, including investigations into one test or two conditionally independent or dependent tests. We focus on a general setting that involves non-identifiable models for data when true disease status is unavailable due to the nonexistence of or undesirable side effects from a perfectly accurate (i.e. ‘gold standard’) test; special cases of the general method apply to identifiable models with or without gold-standard data. Calculation of Bayes factors is performed by incorporating prior information for model parameters (e.g. sensitivity, specificity, and disease prevalence) and augmenting the observed test-outcome data with unobserved latent data on disease status to facilitate Gibbs sampling from posterior distributions. We illustrate our methods using a thorough simulation study and an application to toxoplasmosis.     

Designing stratified sampling in economic and business surveys

In most economic and business surveys, the target variables (e.g. turnover of enterprises, income of households, etc.) commonly resemble skewed distributions with many small and few large units. In such surveys, if a stratified sampling technique is used as a method of sampling and estimation, the convenient way of stratification such as the use of demographical variables (e.g. gender, socioeconomic class, geographical region, religion, ethnicity, etc.) or other natural criteria, which is widely practiced in economic surveys, may fail to form homogeneous strata and is not much useful in order to increase the precision of the estimates of variables of interest. In this paper, a stratified sampling design for economic surveys based on auxiliary information has been developed, which can be used for constructing optimum stratification and determining optimum sample allocation to maximize the precision in estimate.     

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.