Living in a nation,a state or a place? The Protestant gentry of County Cork

The communal narrative of the Irish state defined the criteria for membership of the nation in opposition to the characteristics of the Irish gentry class, whether in their Protestantism, Anglicised lifestyles or external focus on Britain and its empire. The result ensured that while all Protestants engaged with the institutions of state, their inclusion in the nation proved much more difficult to negotiate, and for those with a gentry background, it would appear an impossibility. This article argues, in contrast, that the remnants of the Protestant gentry class successfully insert themselves into an alternative conception of the Irish nation that draws directly upon the longevity of their family association with specific localities in the Irish landscape.     

Biblical nationalism and the sixteenth-century states

The emergence of Protestant nations in sixteenth-century Europe was driven by the sudden rediscovery of biblical nationalism, a political model that did not separate the religious from the political. Biblical nationalism was new because pre-Reformation Europeans encountered the Hebrew Bible through paraphrases and abridgments. Full-text Bibles revealed a programmatic nationalism backed by unmatched authority as the word of God to readers primed by Reformation theology to seek models in the Bible for the reform of their own societies. Sixteenth-century biblical nationalism was the unintended side effect of a Reformation intended to save souls.     

密度分布非均匀的反馈控制Logistic单种群模型平衡态的稳定性

本文对种群密度在非均匀分布情形下,考虑了具反馈控制的滞后 Logistic 生态模型平衡位置的稳定性;分别给出了在常时滞和弱连续时滞以及强连续时滞情况下的稳定性条件;其结果是对 Gopalsamy 在密度均匀分布情形下相应结果的推广.     

从供应链分析探讨传统出口型外贸企业的发展战略

从分析传统外贸企业在国际供应链中的地位入手,研究了传统外贸企业的现有功能和功能扩展空间,从而提出传统外贸企业应该坚持加强供应链管理、加强战略合作伙伴关系的建设,并努力实现信息化的战略发展方向。     

人机工程学的发展及人机系统的可靠性

本文介绍了人机工程学(工效学)这一新兴边缘学科的研究内容、发展及现状,讨论了人-机系统的可靠性并着重讨论了人的操作可靠性,探讨了按人为差错进行可靠性分析与可靠性估计的人为差错和可靠性分析逻辑推演法(HERALD法)及系统能力方法(SC方法)。 本文的结论可供系统管理工作者及设计工作者,特别是可靠性管理及可靠性设计工作者参考。     

Error rates in classification consisting of discrete and continuous variables in the presence of covariates

We consider classifying an object based on mixed continuous and discrete variables between two populations. Mixed discrete and continuous covariates with identical means in both populations are amongst the variables. Under the location model with homogeneous location specific conditional dispersion matrices for both populations, the Bayes rule is given. Classification is implemented by a plug-in version of the Bayes rule with full covariate adjustment. An asymptotic expansion of the overall expected error of the procedure is derived. Our findings generalize several classical results.     

Estimation with right‐censored observations under a semi‐Markov model

The semi‐Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end‐point of the support of the censoring time is strictly less than the right end‐point of the support of the semi‐Markov kernel, the transition probability of the semi‐Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi‐Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study. The Canadian Journal of Statistics 41: 237–256; 2013 © 2013 Statistical Society of Canada     

Forecasting Multivariate Road Traffic Flows Using Bayesian Dynamic Graphical Models,Splines and Other Traffic Variables

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simple model to use. While statistical flow forecasting models usually base their forecasts on flow data alone, data for other traffic variables are also routinely collected. This paper shows how cubic splines can be used to incorporate extra variables into the LMDM in order to enhance flow forecasts. Cubic splines are also introduced into the LMDM to parsimoniously accommodate the daily cycle exhibited by traffic flows. The proposed methodology allows the LMDM to provide more accurate forecasts when forecasting flows in a real high‐dimensional traffic data set. The resulting extended LMDM can deal with some important traffic modelling issues not usually considered in flow forecasting models. Additionally, the model can be implemented in a real‐time environment, a crucial requirement for traffic management systems designed to support decisions and actions to alleviate congestion and keep traffic flowing.     

Developing a variables repetitive group sampling plan based on process capability index C pk with unknown mean and variance

In this paper, variables repetitive group sampling plans are developed based on the process capability index C _pk when the quality characteristic follows a normal distribution with unknown mean and variance. The sampling plan parameters such as the sample size and the acceptance constant are determined to minimize the average sample number. Symmetric and asymmetric cases, in percent defectives due to two specification limits, are dealt with for specified combinations of acceptable quality level and limiting quality level. Tables are provided and examples are given to use proposed plans in practice.     

Accuracy Analysis of the Percentile Method for Estimating Non Normal Manufacturing Quality

We consider asymmetric kernel estimates based on grouped data. We propose an iterated scheme for constructing such an estimator and apply an iterated smoothed bootstrap approach for bandwidth selection. We compare our approach with competing methods in estimating actuarial loss models using both simulations and data studies. The simulation results show that with this new method, the estimated density from grouped data matches the true density more closely than with competing approaches.