Bayesian prediction of waiting times in stochastic models

The authors show how saddlepoint techniques lead to highly accurate approximations for Bayesian predictive densities and cumulative distribution functions in stochastic model settings where the prior is tractable, but not necessarily the likelihood or the predictand distribution. They consider more specifically models involving predictions associated with waiting times for semi‐Markov processes whose distributions are indexed by an unknown parameter θ. Bayesian prediction for such processes when they are not stationary is also addressed and the inverse‐Gaussian based saddlepoint approximation of Wood, Booth & Butler (1993) is shown to accurately deal with the nonstationarity whereas the normal‐based Lugannani & Rice (1980) approximation cannot, Their methods are illustrated by predicting various waiting times associated with M/M/q and M/G/1 queues. They also discuss modifications to the matrix renewal theory needed for computing the moment generating functions that are used in the saddlepoint methods.     

Non-asymptotic sequential confidence regions with fixed sizes for the multivariate nonlinear parameters of regression

In this paper we consider a sequential design for the estimation of nonlinear parameters of regression with guaranteed accuracy. Non-asymptotic confidence regions with fixed sizes for the least squares estimates are used. The obtained confidence region is valid for finite numbers of data points when the distributions of the observations are unknown.     

A robust multivariate measurement error model with skew-normal/independent distributions and Bayesian MCMC implementation

Skew-normal/independent distributions are a class of asymmetric thick-tailed distributions that include the skew-normal distribution as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in multivariate measurement errors models. We propose the use of skew-normal/independent distributions to model the unobserved value of the covariates (latent variable) and symmetric normal/independent distributions for the random errors term, providing an appealing robust alternative to the usual symmetric process in multivariate measurement errors models. Among the distributions that belong to this class of distributions, we examine univariate and multivariate versions of the skew-normal, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.     

Nonparametric control charts based on runs and Wilcoxon-type rank-sum statistics

In this article, we introduce three new distribution-free Shewhart-type control charts that exploit run and Wilcoxon-type rank-sum statistics to detect possible shifts of a monitored process. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of these charts is that, due to their nonparametric nature, the false alarm rate (FAR) and in-control run length distribution is the same for all continuous process distributions. Tables are provided for the implementation of the charts for some typical FAR values. Furthermore, a numerical study carried out reveals that the new charts are quite flexible and efficient in detecting shifts to Lehmann-type out-of-control situations.     

Adaptive weights smoothing with applications to image restoration

We propose a new method of nonparametric estimation which is based on locally constant smoothing with an adaptive choice of weights for every pair of data points. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on some simulated univariate and bivariate examples and compare it with other nonparametric methods. Finally we discuss applications of this procedure to magnetic resonance and satellite imaging.     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Construction and selection of quick switching double-sampling system: sample size tightening

A new sampling inspection system involving normal and tightened plans is presented. Performance measures are developed. The average sample number of the newly developed system is compared with those of existing plans. Procedures are indicated an tables are constructed for designing the system indexed by various combinations of parameters.     

Inequalities for variances of some functions of random variables

In this note we prove some inequalities for variances and other measures of deviation of functions of random variables. Based on these inequalities we find some corollaries concerning the variances of fractional powers of random variables and of sums of independent random variables.     

论现代逻辑的应用

现代逻辑的价值和意义,在于其在理论上的应用。对于纯数学的基础和结构来说,现代逻辑是一种非常有用的工具,包括哥德尔不完全性定理等,都利用了现代逻辑的技术和成果。某些现代逻辑技术已经广泛应用到了工程学之中,例如,计算机工程就基于现代逻辑的高级成果。反过来,这些应用又刺激了逻辑理论本身取得更多的进步。     

中国出现新乡村性？——基于长江三角洲地区乡村旅游发展的研究

在中国,越来越多城市居民喜欢利用周末及节假日前往乡村地区休闲度假,给旅游地理学和休闲地理学研究带来了新的研究方向,为研究中国新型的甚至是未知的城乡关系提供了新的视角。这种新的休闲度假趋势主要产生于大中型城市周边的乡村地区。以位于长江三角洲的上海周边郊区为例,研究新型乡村旅游在城郊地区的发展,在保留和修缮传统乡村景观的同时,也产生出新型景观。旅游的发展促使乡村地区结构重组,建立了乡村旅游从业者的经营活动与当地农业发展之间的新联系。从中区分出两种乡村旅游经营模式:一种是为了适应当地乡村现代化发展需要并在当地政府支持下发展起来的乡村旅游示范村;另一种则是由个人开发且相对独立的小型农家乐或乡村旅游景点。这些地区乡村旅游的快速发展既有利于当地发展支柱产业,调整产业结构,扩大经济活动范围,也为城市居民重新审视乡村世界提供新视角。