A probabilistic clustering method for data elements with normal distributed attributes

By considering uncertainty in the attributes common methods cannot be applicable in data clustering. In the recent years, many researches have been done by considering fuzzy concepts to interpolate the uncertainty. But when data elements attributes have probabilistic distributions, the uncertainty cannot be interpreted by fuzzy theory. In this article, a new concept for clustering of elements with predefined probabilistic distributions for their attributes has been proposed, so each observation will be as a member of a cluster with special probability. Two metaheuristic algorithms have been applied to deal with the problem. Squared Euclidean distance type has been considered to calculate the similarity of data elements to cluster centers. The sensitivity analysis shows that the proposed approach will converge to the classic approaches results when the variance of each point tends to be zero. Moreover, numerical analysis confirms that the proposed approach is efficient in clustering of probabilistic data.     

A direct procedure for monitoring the coefficient of variation using a variable sample size scheme

A variable sample size (VSS) scheme directly monitoring the coefficient of variation (CV), instead of monitoring the transformed statistics, is proposed. Optimal chart parameters are computed based on two criteria: (i) minimizing the out-of-control ARL (ARL₁) and (ii) minimizing the out-of-control ASS (ASS₁). Then the performances are compared between these two criteria. The advantages of the proposed chart over the VSS chart based on the transformed statistics in the existing literature are: the former (i) provides an easier alternative as no transformation is involved and (ii) requires less number of observations to detect a shift when ASS₁ is minimized.     

Monitoring the coefficient of variation using a variable sample size and sampling interval control chart

This article proposes a CV chart by using the variable sample size and sampling interval (VSSI) feature to improve the performance of the basic CV chart, for detecting small and moderate shifts in the CV. The proposed VSSI CV chart is designed by allowing the sample size and the sampling interval to vary. The VSSI CV chart's statistical performance is measured by using the average time to signal (ATS) and expected average time to signal (EATS) criteria and is compared with that of existing CV charts. The Markov chain approach is employed in the design of the chart.     

Parametric bootstrap inferences for the growth curve models with intraclass correlation structure

This paper presents parametric bootstrap (PB) approaches for hypothesis testing and interval estimation of the fixed effects and the variance component in the growth curve models with intraclass correlation structure. The PB pivot variables are proposed based on the sufficient statistics of the parameters. Some simulation results are presented to compare the performance of the proposed approaches with the generalized inferences. Our studies show that the PB approaches perform satisfactorily for various cell sizes and parameter configurations, and tends to outperform the generalized inferences with respect to the coverage probabilities and powers. The PB approaches not only have almost exact coverage probabilities and Type I error rates, but also have the shorter expected lengths and the higher powers. Furthermore, the PB procedure can be simply carried out by a few simulation steps. Finally, the proposed approaches are illustrated by using a real data example.     

Optimal and lead-in adaptive allocation for binary outcomes: A comparison of Bayesian methods

We compare posterior and predictive estimators and probabilities in response-adaptive randomization designs for two- and three-group clinical trials with binary outcomes. Adaptation based upon posterior estimates are discussed, as are two predictive probability algorithms: one using the traditional definition, the other using a skeptical distribution. Optimal and natural lead-in designs are covered. Simulation studies show that efficacy comparisons lead to more adaptation than center comparisons, though at some power loss, skeptically predictive efficacy comparisons and natural lead-in approaches lead to less adaptation but offer reduced allocation variability. Though nuanced, these results help clarify the power-adaptation trade-off in adaptive randomization.     

The discrepancy of P-values and posterior probability: Two-sided hypothesis of variance of normal distribution with unknown mean

This article is concerned with the comparison of P-value and Bayesian measure in point null hypothesis for the variance of Normal distribution with unknown mean. First, using fixed prior for test parameter, the posterior probability is obtained and compared with the P-value when an appropriate prior is used for the mean parameter. In the second, lower bounds of the posterior probability of H₀ under a reasonable class of prior are compared with the P-value. It has been shown that even in the presence of nuisance parameters, these two approaches can lead to different results in the statistical inference.     

论科学证据的两种概率解释

在科学证据研究中,存在＂正相关＂和＂高概率＂两种科学证据的概率定义。从这两个定义出发,能够比较清晰直观地解释科学证据的许多相关问题,如科学证据中的＂比较级＂和＂可接受性＂等。然而,两个定义自身存在着不可忽视的缺陷：＂正相关＂不是证据的充要条件,＂高概率＂也不足以使证据之为证据。     

网络侵权投诉的平台审核及边界

现行法律规定网络平台侵权投诉（即通知）的形式审查,在机制目的设置上,旨在降低平台实质审查带来的高成本。然而,过于简单的形式审查要件之规定对平台的稳定性与人力审核成本反而提出了更高的要求。为维护平台信息数据的稳定性,平台难免会选择实质审查来减少形式审查所产生的信息波动。这又与网络平台侵权投诉形式审查的宗旨相悖。平台针对不同的权利类型的投诉进行审核,其中最为核心的就是对“构成侵权的初步证明材料”的把握。这个过程是个主观大于客观的自我内心确信。在以高度盖然性为标准的大前提下,网络平台要做的是权利归属的恰当性判断。     

单窃听双跳协作网络的中继选择方案及其性能分析

研究了安全通信意义下,单向译码转发（decode-and-forward,DF）协作无线网络的中继选择问题。针对窃听者既能获得信源发出的信号,又能窃取中继节点转发数据的通信系统,提出了3种中继选择方案来对抗窃听者,增强系统物理层安全性。其中,方案一选择到窃听者信噪比（signal-to-noise ratio,SNR）最小的中继节点;方案二为最大最小（max-min）选择方案,即选择信源到中继节点和中继节点到信宿的较差信噪比中最大值所对应的中继节点;方案三根据窃听信道和主信道的瞬时信道状态信息（channel state information,CSI）选择使得窃听网络有最大保密容量的中继节点。在对各方案的性能分析过程中,得到了各中继选择方案拦截概率的闭式表示,进一步对拦截概率作渐近分析,获得了各中继选择方案的分集阶数。具体地,方案一的分集阶数为1,另外2个中继选择方案的分集阶数均为中继节点个数M。数值结果验证了理论分析得到的结论。     

Confidence and prediction intervals based on interpolated records

In several statistical problems, nonparametric confidence intervals for population quantiles can be constructed and their coverage probabilities can be computed exactly, but cannot in general be rendered equal to a pre-determined level. The same difficulty arises for coverage probabilities of nonparametric prediction intervals for future observations. One solution to this difficulty is to interpolate between intervals which have the closest coverage probability from above and below to the pre-determined level. In this paper, confidence intervals for population quantiles are constructed based on interpolated upper and lower records. Subsequently, prediction intervals are obtained for future upper records based on interpolated upper records. Additionally, we derive upper bounds for the coverage error of these confidence and prediction intervals. Finally, our results are applied to some real data sets. Also, a comparison via a simulation study is done with similar classical intervals obtained before.