On nonlinear models for time series

The paper is a review of nonlinear processes used in time series analysis and presents some new original results about stationary distribution of a nonlinear autoregres-sive process of the first order. The following models are considered: nonlinear autoregessive processes, threshold AR processes, threshold MA processes, bilinear models, auto-regressive models with random parameters including double stochastic models, exponential AR models, generalized threshold models and smooth transition autoregressive models, Some tests for linearity of processes are also presented.     

Frailty modeling for clustered survival data: an application to birth interval in Bangladesh

The present work demonstrates an application of random effects model for analyzing birth intervals that are clustered into geographical regions. Observations from the same cluster are assumed to be correlated because usually they share certain unobserved characteristics between them. Ignoring the correlations among the observations may lead to incorrect standard errors of the estimates of parameters of interest. Beside making the comparisons between Cox's proportional hazards model and random effects model for analyzing geographically clustered time-to-event data, important demographic and socioeconomic factors that may affect the length of birth intervals of Bangladeshi women are also reported in this paper.     

Distance Between Random Points in Two Rectangular Cities

ABSTRACT

The travel distance between two cities of rectangular shape is considered. Two uniformly distributed random points, one from each city, are taken. Their straight path travel distance is then measured. The explicit forms for the probability density function of this distance and its expected value are obtained. Numerical results of calculating the exact expected distance and the estimated distance as well as computer simulation are given for various cases. The integer moments of the distance are also discussed.     

Bayesian Estimation Using Warner's Randomized Response Model through Simple and Mixture Prior Distributions

The linear discriminant function (LDF) is known to be optimal in the sense of achieving an optimal error rate when sampling from multivariate normal populations with equal covariance matrices. Use of the LDF in nonnormal situations is known to lead to some strange results. This paper will focus on an evaluation of misclassification probabilities when the power transformation could have been used to achieve at least approximate normality and equal covariance matrices in the sampled populations for the distribution of the observed random variables. Attention is restricted to the two-population case with bivariate distributions.     

On Confidence Intervals for Process Capability Indices in a One-Way Random Model

In this article, we investigated the bootstrap calibrated generalized confidence limits for process capability indices C _pk for the one-way random effect model. Also, we derived Bissell's approximation formula for the lower confidence limit using Satterthwaite's method and calculated its coverage probabilities and expected values. Then we compared it with standard bootstrap (SB) method and generalized confidence interval method. The simulation results indicate that the confidence limit obtained offers satisfactory coverage probabilities. The proposed method is illustrated with the help of simulation studies and data sets.     

Singular Gaussian graphical models: Structure learning

ABSTRACT

The goal of this article is to introduce singular Gaussian graphical models and their conditional independence properties. In fact, we extend the concept of Gaussian Markov Random Field to the case of a multivariate normally distributed vector with a singular covariance matrix. We construct, then, the associated graph’s structure from the covariance matrix’s pseudo-inverse on the basis of a characterization of the pairwise conditional independence. The proposed approach can also be used when the covariance matrix is ill-conditioned, through projecting data on a smaller subspace. In this case, our method ensures numerical stability and consistency of the constructed graph and significantly reduces the inference problem’s complexity. These aspects are illustrated using numerical experiments.     

莫沙比利对老年慢性心衰患者胆囊运动功能影响的临床研究

目的研究老年慢性心衰患者是否存在胆囊运动功能障碍及探讨莫沙比利对胆囊运动功能的影响。方法用实时超声显像法检测30例老年慢性心衰患者经莫沙比利治疗前后及20例正常对照组的胆囊容积,同时计算其胆囊排空指数（GBEF）。结果正常对照组的空腹胆囊容积为（18．64±2．26）ml,老年慢性心衰组为（22．02±3．48）ml,二者比较有显著性差异（P〈0．01）;老年慢性心衰组在脂餐后10、30、60min胆囊排空分数均显著低于正常对照组（P〈0．01）。通过莫沙比利治疗四周后,老年慢性心衰组空腹胆囊容积为（19．86±2．32）ml,显著小于治疗前（P〈0．05）,却仍然大于正常组（P〈0．05）,而脂餐后10、30、60min胆囊排空分数均显著增大（P〈0．01）,但仍然小于正常对照组（P〈0．01）。结论老年慢性心衰患者不仅存在胃肠运动功能障碍,而且还存在明显的胆囊运动功能障碍;胃肠促动力药莫沙比利能明显改善老年慢性心衰患者的胆囊运动功能。     

独立学院人才失效的故障树分析

将人才培养作为一项系统工程,学生不能完成学业视作故障或者失效.应用可靠性理论,从故障树的分析入手,利用布尔代数运算,从独立学院学生、教师、家庭和学校四个方面进行剖析,找出学生不能完成学业的原因,提出促进独立学院学生完成学习任务一系列措施.     

无线共享信道退避访问攻击的新型检测算法

针对无线竞争共享型信道中恶意节点(或自私节点)不公平使用信道资源的退避访问攻击行为,提出一种新的中心极限定理检测算法。该算法能够有效降低计算复杂度,按需调节检测速度和检测精度、并且易于实现分布式联合检测。该文还基于CLT算法建立Markov链多步检测模型,进一步提升对恶意节点的检测精度。仿真试验表明,CLT检测算法具有较低的误判率和较快的检测速度,对介质访问控制(MAC)子层退避攻击行为的检测性能明显优于经典DOMINO算法。     

论非营利组织在公共危机治理中的作用

公共危机由于其与生俱来的突发性、紧迫性、危害性、不确定性及对社会影响的扩散性等特性,使以政府为中心的单一治理模式无力应对其挑战,以政府为主导的多中心治理模式成为不可逆转的趋势。在多中心治理模式下,政府要大力扶持非营利组织的发展,非营利组织也要加强自身建设,注重提高自身形象,树立社会公信力,通过实现政府与非营利组织的良性互动共同治理公共危机。