A new S2 control chart using repetitive sampling

A new S² control chart is presented for monitoring the process variance by utilizing a repetitive sampling scheme. The double control limits called inner and outer control limits are proposed, whose coefficients are determined by considering the average run length (ARL) and the average sample number when the process is in control. The proposed control chart is compared with the existing Shewhart S² control chart in terms of the ARLs. The result shows that the proposed control chart is more efficient than the existing control chart in detecting the process shift.     

CUSUM Control Charts for Multivariate Poisson Distribution

A cumulative sum control chart for multivariate Poisson distribution (MP-CUSUM) is proposed. The MP-CUSUM chart is constructed based on log-likelihood ratios with in-control parameters, Θ₀, and shifts to be detected quickly, Θ₁. The average run length (ARL) values are obtained using a Markov Chain-based method. Numerical experiments show that the MP-CUSUM chart is effective in detecting parameter shifts in terms of ARL. The MP-CUSUM chart with smaller Θ₁ is more sensitive than that with greater Θ₁ to smaller shifts, but more insensitive to greater shifts. A comparison shows that the proposed MP-CUSUM chart outperforms an existing MP chart.     

A comparative study of confidence intervals for negative binomial proportion

In this paper, we investigate four existing and three new confidence interval estimators for the negative binomial proportion (i.e., proportion under inverse/negative binomial sampling). An extensive and systematic comparative study among these confidence interval estimators through Monte Carlo simulations is presented. The performance of these confidence intervals are evaluated in terms of their coverage probabilities and expected interval widths. Our simulation studies suggest that the confidence interval estimator based on saddlepoint approximation is more appealing for large coverage levels (e.g., nominal level≤1% ) whereas the score confidence interval estimator is more desirable for those commonly used coverage levels (e.g., nominal level>1% ). We illustrate these confidence interval construction methods with a real data set from a maternal congenital heart disease study.     

A bootstrap control chart for inverse Gaussian percentiles

The conventional Shewhart-type control chart is developed essentially on the central limit theorem. Thus, the Shewhart-type control chart performs particularly well when the observed process data come from a near-normal distribution. On the other hand, when the underlying distribution is unknown or non-normal, the sampling distribution of a parameter estimator may not be available theoretically. In this case, the Shewhart-type charts are not available. Thus, in this paper, we propose a parametric bootstrap control chart for monitoring percentiles when process measurements have an inverse Gaussian distribution. Through extensive Monte Carlo simulations, we investigate the behaviour and performance of the proposed bootstrap percentile charts. The average run lengths of the proposed percentage charts are investigated.     

The extended GWMA control chart

This study extends the generally weighted moving average (GWMA) control chart by imitating the double exponentially weighted moving average (DEWMA) technique. The proposed chart is called the double generally weighted moving average (DGWMA) control chart. Simulation is employed to evaluate the average run length characteristics of the GWMA, DEWMA and DGWMA control charts. An extensive comparison of these control charts reveals that the DGWMA control chart with time-varying control limits is more sensitive than the GWMA and the DEWMA control charts for detecting medium shifts in the mean of a process when the shifts are between 0.5 and 1.5 standard deviations. Additionally, the GWMA control chart performs better when the mean shifts are below the 0.5 standard deviation, and the DEWMA control performs better when the mean shifts are above the 1.5 standard deviation. The design of the DGWMA control chart is also discussed.     

Bayesian analysis of elapsed times in continuous‐time Markov chains

The authors consider Bayesian analysis for continuous‐time Markov chain models based on a conditional reference prior. For such models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition, an infinitesimal rate matrix also characterizes this class of models. Experts often have good prior knowledge about the parameters of this matrix. The authors show that the use of a proper prior for the rate matrix parameters together with the conditional reference prior for the elapsed time yields a proper posterior distribution. The authors also demonstrate that, when compared to analyses based on priors previously proposed in the literature, a Bayesian analysis on the elapsed time based on the conditional reference prior possesses better frequentist properties. The type of prior thus represents a better default prior choice for estimation software.     

关于单形体积与棱长的两个几何不等式

获得了涉及ｎ维单形的体积与棱长的两个几何不等式,推广了已有文献的有关结果．     

Why do participants in voluntary organizations leave? Exploring the relationship between value congruence and length of stay

Recognizing the importance of values and the specific characteristics of participants and situations in voluntary organizations, we examine how value congruence—the fit or compatibility of values between participants and the organization, or among participants—interacts with personal and situational factors to predict participants' length of stay in their organizations. Introducing the case of a voluntary organization that offers shared housing, we measure value congruence through textual similarity in the self‐introduction documents of 49 participants and the organization's mission statement. This approach differs from the self‐reported measures based on participants' perceptions or recalled interactions used in previous studies. In line with expectation‐disconfirmation theory, participants with the strongest beliefs in organizational values had shorter lengths of stay. The amount of intraorganizational communication also moderated the relationship between value congruence and length of stay. This study provides theoretical and methodological implications for nonprofit management by considering personal and situational factors and evaluating value congruence by textual similarity.     

Hungarian verbal puzzles and the intensity of language contact

Grammaticality judgments of bilingual Hungarians in Slovakia and Ukraine vs. Yugoslavia show that overt objects are much more acceptable in the northern Slavic countries than in Yugoslavia. Two conflicting hypotheses have been advanced to explain this difference. The structural hypothesis claims that Serbian cliticization is responsible, while the socio-historical hypothesis claims that the difference in grammaticality judgments is due to a difference in the duration of Hungarian–Slavic language contact north of and south of present-day Hungary. Data from a seven-country survey (N = 846) show that there is a split between judgments in the northern vs. southern Slavic countries across a wide range of linguistic variables, which discredits the structural explanation for the object pro-drop variable, at least as the sole cause of change. Moreover, statistical analyses of 24 variables provide substantial empirical verification of Thomason and Kaufman's 'two crucial parameters of intensity of contact in a borrowing situation': time and level of bilingualism. It is shown that the 250 years of contact between Hungarian and Serbian has resulted in much smaller contact effects than the thousand-year-old contact of Hungarian with the northern Slavic languages. Bilingual Hungarians who constitute a local minority in the settlements where they live systematically favor the contact-induced variants of variables vis-a`-vis those who constitute a local majority.     

Geometric sample size determination in Bayesian analysis

The problem of sample size determination in the context of Bayesian analysis is considered. For the familiar and practically important parameter of a geometric distribution with a beta prior, three different Bayesian approaches based on the highest posterior density intervals are discussed. A computer program handles all computational complexities and is available upon request.