The Generalized Jackknife Statistic

This paper shows how tabulated exact factors for prediction intervals for all k future observations, under the assumption of normality, readily provide conservative factors for some values of k beyond the range of the tabulations.     

THE MINIMUM ENTROPY PRINCIPLE IS NOT THE PRINCIPLE OF MINIMUM CROSS-ENTROPY

In this current paper the following problems are addressed: (1) extending the knowledge of a partially known probability distribution function to any point of a continuous sample space, (2) constructing an imprecise probability distribution based on the knowledge of a set of credible or confidence intervals, and (3) computing the lower and upper expected values of a random continuous variable. An example is provided.     

Accounting for uncertainty in control-relevant statistics

To make appropriate decisions based on common indices used in control, both the point estimates and their uncertainties must be known. Many control-relevant statistics, such as model predictions, gain margins and other frequency domain quantities, are functions of parameters of process models. Confidence regions for these quantities are most often calculated under the assumption that these quantities have an asymptotic limiting normal distribution. These confidence regions may be erroneous, and very misleading, as the asymptotic results ignore the influence of parameter nonlinearities. In addition, proximity of the model parameters to stability/invertibility boundaries also distorts the confidence regions from those predicted from asymptotic theory. Generalized profiling is a flexible numerical method for constructing confidence intervals and confidence regions for model parameters, and functions of model parameters. Applications in nonlinear regression [D. Bates, D. Watts, Nonlinear Regression Analysis and Its Applications, John Wiley & Sons, New York, 1988] indicate that it provides a much more accurate representation of uncertainty in those instances when the asymptotic uncertainty results are inaccurate or misleading. Generalized profiling is based on the likelihood approach to quantifying uncertainty. The numerical construction of these likelihood uncertainty regions requires solution to a series of constrained optimization problems. Computationally efficient diagnostic tests, motivated by profiling, are developed. These can be effectively employed as screening tools to indicate when the asymptotic results are most likely to be inadequate.     

Beyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis.

This article presents confidence interval methods for improving on the standard F tests in the balanced, completely between-subjects, fixed-effects analysis of variance. Exact confidence intervals for omnibus effect size measures, such as or and the root-mean-square standardized effect, provide all the information in the traditional hypothesis test and more. They allow one to test simultaneously whether overall effects are (a) zero (the traditional test), (b) trivial (do not exceed some small value), or (c) nontrivial (definitely exceed some minimal level). For situations in which single-degree-of-freedom contrasts are of primary interest, exact confidence interval methods for contrast effect size measures such as the contrast correlation are also provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

复杂环境下土石方浅孔微差爆破技术应用与分析

借助两例爆破工程实例，重点介绍了在复杂环境下的土石方浅孔微差控爆起爆方式，分析比较了不同起爆方式特点及微差间隔时间的选择依据。     

论室外气候条件的制约对电子衡器准确度的影响——当前衡器行业最具争议的热点问题论述之一

本文试图从OIMLR76对衡器环境制约因素的规定入手，结合使用现场气候条件的基本概念分析与计算，充分论述了室外气候条件下电子衡器的最大检定分度数n值不能大于3000的理由，并呼吁我国计量监督执法部门与制造厂商、用户应该共同遵循这一国际通用的计量法规。     

Inter‐annual variability,risk and confidence intervals associated with propagation statistics. Part II: parameterization and applications

This set of two companion papers aims at providing a model for the inter‐annual variability of earth‐space propagation statistics and for the inherent risk and CIs. In part I, it was proposed to model the yearly variance σ² of empirical complementary CDFs so that where is the variance of estimation, the inter‐annual climatic variance and p the long‐term probability. Particularly, an analytical formulation of was derived and parameterized from synthetic rain attenuation data. Considering the statistical framework developed in part I, this part II is specifically devoted to the parameterization of the variance of estimation from experimental data of rain attenuation and rainfall rate. Then, a methodology to model and parameterize worldwide the inter‐annual climatic variance is presented. The model of yearly variance of the empirical complementary CDFs is finally compared against yearly experimental variances derived from data collected worldwide. The knowledge of this variability is very useful for system design as it allows the risk on a required availability and associated with a given propagation margin to be quantified. Copyright © 2013 John Wiley & Sons, Ltd.     

ADB610钢冲击功的概率模型及安全冲击功研究

鉴于ADB610钢冲击功(Akv)数据的分散性。采用多种概率统计模型对其进行拟合,并采用相关系数法和柯尔莫哥洛夫～斯米尔诺夫检验法(K-S检验法)检验拟合效果,检验结果显示三参数威布尔分布最优。通过对ADB610钢冲击功置信区间的研究,得到置信度95%、可靠度99.9%的置信区间为[139.5J,217.1J]。最后对冲击功进行求解,以获得一定可靠度下的安全冲击功,分别得到对应可靠度为0.9,0.975和0.99时的安全冲击功228.7J,210.1J和200.7 J。     

Predictive analyses for nonhomogeneous Poisson processes with power law using Bayesian approach

Nonhomogeneous Poisson process (NHPP) also known as Weibull process with power law, has been widely used in modeling hardware reliability growth and detecting software failures. Although statistical inferences on the Weibull process have been studied extensively by various authors, relevant discussions on predictive analysis are scattered in the literature. It is well known that the predictive analysis is very useful for determining when to terminate the development testing process. This paper presents some results about predictive analyses for Weibull processes. Motivated by the demand on developing complex high-cost and high-reliability systems (e.g., weapon systems, aircraft generators, jet engines), we address several issues in single-sample and two-sample prediction associated closely with development testing program. Bayesian approaches based on noninformative prior are adopted to develop explicit solutions to these problems. We will apply our methodologies to two real examples from a radar system development and an electronics system development.     

A Method for Determining Equivalence in Industrial Settings: Defining and Testing the Equivalence of Two Methods or Two Laboratories

A common practice in industrial settings is to determine the equivalence between two methods or two laboratories. Average equivalence is the preferred method. Average equivalence focuses only on the comparison of the means. However, it may be also of interest to compare the variabilities. We propose an equivalence method that considers the means and the variabilities when comparing two methods or two laboratories. We develop bounds for conducting statistical tests using the proposed equivalence criterion. Simulation is conducted to study the performance of the bounds. The criteria are the ability to maintain the stated confidence level, as well as the stated test size, and the simulated power of the tests using these bounds. Bounds that perform well for small sample size are also desirable.