Simultaneous structure estimation and variable selection in partial linear varying coefficient models for longitudinal data

Partial linear varying coefficient models (PLVCM) are often considered for analysing longitudinal data for a good balance between flexibility and parsimony. The existing estimation and variable selection methods for this model are mainly built upon which subset of variables have linear or varying effect on the response is known in advance, or say, model structure is determined. However, in application, this is unreasonable. In this work, we propose a simultaneous structure estimation and variable selection method, which can do simultaneous coefficient estimation and three types of selections: varying and constant effects selection, relevant variable selection. It can be easily implemented in one step by employing a penalized M-type regression, which uses a general loss function to treat mean, median, quantile and robust mean regressions in a unified framework. Consistency in the three types of selections and oracle property in estimation are established as well. Simulation studies and real data analysis also confirm our method.     

A new look at clustering coefficients with generalization to weighted and multi-faction networks

In this paper we propose a new method for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks the values obtained for commonly considered social networks are in sharp contrast to those obtained, for instance, by the method of Opsahl (2013), and provide a different viewpoint for clustering. The approach is also applicable in questions related to the general study of segregation and homophily. Applications to existent data sets are considered.     

Structural Change Monitoring for Random Coefficient Autoregressive Time Series

A monitoring scheme is proposed to sequentially detect a structural change in random coefficient autoregressive time series of order p (RCA(p)) after a training period of size T. It extends structural change monitoring to RCA(p) time series. The asymptotic properties of our monitoring statistic are established under both the null of no change in parameters and the alternative of a change in coefficient. The finite sample properties are investigated by a simulation study.     

Nonparametric estimator for mean residual life and vitality function

In this paper, we study the estimation of the vitality function(v(x)=E(X|X>x) and mean residual life function(e(x)=E(X-x|X>x) from a sample ofX using the empirical estimator and kernel estimator. Under suitable conditions of regularity, the asymptotic normality of the kernel estimator is obtained. Partially supported by Consejeria de Cultura y Ed. (C.A.R.M.), under Grant PIB 95/90.     

Failure Inference From a Marker Process Based on a Bivariate Wiener Model

Many models have been proposed that relate failure times and stochastic time-varying covariates. In some of these models, failure occurs when a particular observable marker crosses a threshold level. We are interested in the more difficult, and often more realistic, situation where failure is not related deterministically to an observable marker. In this case, joint models for marker evolution and failure tend to lead to complicated calculations for characteristics such as the marginal distribution of failure time or the joint distribution of failure time and marker value at failure. This paper presents a model based on a bivariate Wiener process in which one component represents the marker and the second, which is latent (unobservable), determines the failure time. In particular, failure occurs when the latent component crosses a threshold level. The model yields reasonably simple expressions for the characteristics mentioned above and is easy to fit to commonly occurring data that involve the marker value at the censoring time for surviving cases and the marker value and failure time for failing cases. Parametric and predictive inference are discussed, as well as model checking. An extension of the model permits the construction of a composite marker from several candidate markers that may be available. The methodology is demonstrated by a simulated example and a case application.     

南京市基层卫生人力资源的配置与公平性研究

目的：探讨南京市社区卫生人力资源配置的发展情况和公平性,为重塑新医改背景下的社区卫生服务机构定位提供政策建议。方法：以南京市2011—2014年城市社区卫生工作年报和《南京市卫生统计年鉴》为基础,从社区卫生服务人力数量、学历、职称、医护比、全科医生数量等方面分析南京市社区卫生人力配置发展情况,并运用基尼系数法对南京市社区卫生人力资源配置的公平性进行评估。结果：南京市社区卫生机构卫技人员专业素质不高,医护比例不合理,全科医生数量少且有逐年减少趋势,人力资源按人口分布相对公平,各行政区之间有差异。结论：创新社区卫生人才培养模式,采取有效措施增加社区卫生服务机构的护士数量,健全全科医生人才的培养和培训制度。     

长输管道故障树及可靠性增长对策

天然气大部分通过远距离运输和销售,管道运行的可靠性对天然气市场具有重要的影响。分析讨论了影响道运行可靠性的主要因素,并据此提出提高管道运行可靠性的对策与措施,供管道运行管理者参考。     

基于机会成本的终端用户可靠性电价测算

文章基于可靠性电价"高质高价"的市场化原则,根据供电可靠率和限电序位表,运用机会成本的原理,设计出终端用户的加权可靠率指标,作为用户可靠性电价的成本分摊依据,从而构建出终端用户的可靠性电价测算模型.文章以国内某地区电网公司的实际数据,选取高中低不同可靠性的样本用户,测算了各类用户适用的可靠性电价.研究结果表明,实施终端用户可靠性电价的改革有助于提高整个社会的用电经济效益;测算结果对于中国优化销售电价结构的市场化改革具有实践上的参考价值.     

教育问卷调查的测量学思考

教育问卷调查法是一种教育研究的常用方法。但该方法在使用过程中往往存在着一些问题 ,影响了问卷调查法的科学性。本文认为重视问卷设计中的理论构思 ,使问卷的量化过程符合测理学原理 ,并对问卷进行信度和效度的考证 ,将有助于提高问卷调查法的科学性