Point estimation of lower quantiles based on two-sampling scheme

ABSTRACT

Consider a two-sampling scheme in which an initial sample is first taken from the underlying population and then by assuming a suitable restriction on this sample, some more data points are observed as a new restricted sample. This sampling scheme is used to do inference about the lower quantiles of the underlying distribution. The results are compared with those of simple random sampling in view of mean squared error and Pitman’s measure of closeness criteria for exponential and uniform distributions. It will be shown that the proposed sampling scheme would improve the performance of the point estimators of the lower quantiles of the population.     

Superiority comparisons between mixed regression estimators

This paper gives necessary and sufficient conditions for a mixed regression estimator to be superior to another mixed estimator. The comparisons are based on the mean square error matrices of the estimators. Both estimators are allowed to be biased.     

The Economic Efficiency of Sampling Size: The Case of Beef Trim

The economically optimal sample size in a food safety test balances the marginal costs and marginal benefits of increasing the sample size. We provide a method for selecting the sample size when testing beef trim for Escherichia coli O157:H7 that equates the averted costs of recalls and health damages from contaminated meats sold to consumers with the increased costs of testing while allowing for uncertainty about the underlying prevalence rates of contamination. Using simulations, we show that, in most cases, the optimal sample size is larger than the current sample size of 60 and, in some cases, it exceeds 120. Moreover, lots with a lower prevalence rate have a higher expected damage because contamination is more difficult to detect. Our simulations indicate that these lots have a higher optimal sampling rate.     

Kernel Density Estimation with Generalized Binning

We propose kernel density estimators based on prebinned data. We use generalized binning schemes based on the quantiles points of a certain auxiliary distribution function. Therein the uniform distribution corresponds to usual binning. The statistical accuracy of the resulting kernel estimators is studied, i.e. we derive mean squared error results for the closeness of these estimators to both the true function and the kernel estimator based on the original data set. Our results show the influence of the choice of the auxiliary density on the binned kernel estimators and they reveal that non-uniform binning can be worthwhile.     

两《唐书·李邕传》正补

新旧《唐书·李邕传》对传主叙述互有异同， 并有疏谬。对照两传并利用其它文献， 可以确定李邕的籍里和生卒年； 证明他“补益《文选注》”是讹误； 考辨他仕途坎坷遭遇悲惨不是因为他险躁矜狂， 而主要由于封建官场倾轧与残酷， 他性格主要是刚毅忠烈， 英风豪气； 补充他与李白、杜甫、崔颢等盛唐文坛巨子的交往事迹， 由此表现出的长者风范， 以及他诗文书法作品数量及流传情况     

多孔介质中可压缩混溶驱动问题的特征：混合元方法

本文考虑二维一般有界区域中的多孔介质同可压缩混溶动问题的数值解方法，给出了全离散特征－混合元格，证明了格式的唯一可解性，得到最佳Ｈ－模误差估计。     

Estimation of Error and Bias in Bayesian Monte Carlo Decision Analysis Using the Bootstrap

Bayesian Monte Carlo (BMC) decision analysis adopts a sampling procedure to estimate likelihoods and distributions of outcomes, and then uses that information to calculate the expected performance of alternative strategies, the value of information, and the value of including uncertainty. These decision analysis outputs are therefore subject to sample error. The standard error of each estimate and its bias, if any, can be estimated by the bootstrap procedure. The bootstrap operates by resampling (with replacement) from the original BMC sample, and redoing the decision analysis. Repeating this procedure yields a distribution of decision analysis outputs. The bootstrap approach to estimating the effect of sample error upon BMC analysis is illustrated with a simple value-of-information calculation along with an analysis of a proposed control structure for Lake Erie. The examples show that the outputs of BMC decision analysis can have high levels of sample error and bias.     

一种简单的Internet级联故障模型

介绍了Internet动力学和拓扑特征,分析了引发Internet级联故障的原因,提出了一种简单的Internet级联故障模型(ICFM).仿真实验验证了Internet存在的自组织临界特性,并进一步探讨了该特性可能的成因.该模型将有助于对Internet级联故障的检测和控制技术的研究.     

Approximate Likelihoods for Generalized Linear Errors-in-variables Models

When measurement error is present in covariates, it is well known that naïvely fitting a generalized linear model results in inconsistent inferences. Several methods have been proposed to adjust for measurement error without making undue distributional assumptions about the unobserved true covariates. Stefanski and Carroll focused on an unbiased estimating function rather than a likelihood approach. Their estimating function, known as the conditional score, exists for logistic regression models but has two problems: a poorly behaved Wald test and multiple solutions. They suggested a heuristic procedure to identify the best solution that works well in practice but has little theoretical support compared with maximum likelihood estimation. To help to resolve these problems, we propose a conditional quasi-likelihood to accompany the conditional score that provides an alternative to Wald's test and successfully identifies the consistent solution in large samples.     

红磷烟雾对10.6μm激光的消光分析

本文简要叙述了红磷形成烟雾的过程及性质,并用 Mie 理论计算了不同温度条件下红磷烟雾对10.6μm 激光的质量消化系数α_c 和质量散射系数α。理论计算与实验测得的α_c 有较好的一致性。