多波段光学探测系统的探测概率研究

针对光学联合探测子系统探测概率的不同特点,提出一种新的系统工作模式,使系统的最终探测概率有较大提高。当子系统的探测概率均比较低时,直接对子系统的探测数据进行融合,可以获得较高探测概率;当各子系统探测概率相差较大时,可先对探测数据进行选择,然后再进行数据融合,这样可以在总探测概率减小不大的情况下,使虚警概率明显降低。     

初等概率论中的收敛性注记

通过介绍初等概率论中的四种收敛性 ,揭示了它们之间的相互关系     

Human Reliability Analysis by Fuzzy "CREAM"

This article proposes a systematic procedure for computing probabilities of operator action failure in the cognitive reliability and error analysis method (CREAM). The starting point for the quantification is a previously introduced fuzzy version of the CREAM paradigm that is here further extended to account for: (1) the ambiguity in the qualification of the conditions under which the action is performed (common performance conditions, CPCs) and (2) the fact that the effects of such conditions on human performance reliability may not all be equal.     

服务台需要检修的M／PH／1排队模型分析

本文研究了一个M／PH／1排队,这个排队系统的服务台每服务完一定量的顾客，就要进行一闪检修，然后才继续服务。在检修时间服从位相（PH）分布时，本文求出了这个系统达到平衡的条件以及有关排队率和可靠性的指标。顺便求出了普通的M／PH／1系统第M个顾客离去时刻的分布。     

研究生招生工作中的小概率事件及其预防

利用小概率原理分析研究生招生工作中的突发事件,提出了导致小概率事件发生的原因有重复效应、数量累积效应和群众检验效应,认为在招生工作中要树立小概率事件意识,科学设计招生工作流程,建立应对小概率事件的制度,以更有效地应对小概率事件。     

Bayesian Variable Selection Under Collinearity

In this article, we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of the posterior distribution based on marginal and joint distributions may give conflicting results for assessing the importance of strongly correlated covariates. The natural question is which one should be used in practice. The simulation studies suggest that posterior inclusion probabilities and Bayes factors that evaluate the importance of correlated covariates jointly are more appropriate, and some priors may be more adversely affected in such a setting. To obtain a better understanding behind the phenomenon, we study some toy examples with Zellner’s g-prior. The results show that strong collinearity may lead to a multimodal posterior distribution over models, in which joint summaries are more appropriate than marginal summaries. Thus, we recommend a routine examination of the correlation matrix and calculation of the joint inclusion probabilities for correlated covariates, in addition to marginal inclusion probabilities, for assessing the importance of covariates in Bayesian variable selection.     

Comparing search and estimation performances of designs based on a compound criterion

Since the introduction of the search design by Srivastava [Designs for searching non-negligible effects. In: Srivastava, editor. A survey of statistical design and linear models. Amsterdam: North-Holland, Elsevier; 1975. p. 507–519], construction of such designs has been considered by many researchers. The efficient performances of constructed search designs in terms of parameter estimation and search ability of parameters have also been investigated by several authors. They have proposed suitable optimality measures such as DD- and AD-optimality for estimation in the early stage of search design construction. Moreover, since 1990s, some criteria have been developed to evaluate search performance of a design. Although these criteria are useful none of them is able to evaluate both estimation and search efficiency of a design simultaneously. In this paper, we propose dual-task criteria to deal with searching and estimating performances of search designs. These compound criteria are weighted multiplication of estimation and search suitable criteria. They will be used for design comparison and the results will be presented.     

Evaluation of program success for programs with multiple trials in binary outcomes

A late‐stage clinical development program typically contains multiple trials. Conventionally, the program's success or failure may not be known until the completion of all trials. Nowadays, interim analyses are often used to allow evaluation for early success and/or futility for each individual study by calculating conditional power, predictive power and other indexes. It presents a good opportunity for us to estimate the probability of program success (POPS) for the entire clinical development earlier. The sponsor may abandon the program early if the estimated POPS is very low and therefore permit resource savings and reallocation to other products. We provide a method to calculate probability of success (POS) at an individual study level and also POPS for clinical programs with multiple trials in binary outcomes. Methods for calculating variation and confidence measures of POS and POPS and timing for interim analysis will be discussed and evaluated through simulations. We also illustrate our approaches on historical data retrospectively from a completed clinical program for depression. Copyright © 2015 John Wiley & Sons, Ltd.     

Notes on estimation of the intraclass correlation under the AB/BA crossover trial

Under the AB/BA crossover trial, we focus our attention on estimation of the intraclass correlation in normal data. We develop both point and interval estimators in closed form for the intraclass correlation. We employ Monte Carlo simulation to study the performance of these estimators in a variety of situations. We note that the estimators developed here for the intraclass correlation remain valid even when there are possibly unexpected carry-over effects.     

Construction of Simultaneous Confidence Intervals for Ratios of Means of Lognormal Distributions

For constructing simultaneous confidence intervals for ratios of means for lognormal distributions, two approaches using a two-step method of variance estimates recovery are proposed. The first approach proposes fiducial generalized confidence intervals (FGCIs) in the first step followed by the method of variance estimates recovery (MOVER) in the second step (FGCIs–MOVER). The second approach uses MOVER in the first and second steps (MOVER–MOVER). Performance of proposed approaches is compared with simultaneous fiducial generalized confidence intervals (SFGCIs). Monte Carlo simulation is used to evaluate the performance of these approaches in terms of coverage probability, average interval width, and time consumption.