An Additive Hazards Model for Clustered Recurrent Gap Times

In this article, clustered recurrent gap time is investigated. A marginal additive hazards model is proposed without specifying the association of the individuals within the same cluster. The relationship among the gap times for the same individual is also left unspecified. An estimating equation-based inference procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. In addition, a lack-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease (CGD) is illustrated.     

Variable selection for recurrent event data with informative censoring

Recurrent events data with a terminal event(e.g.,death) often arise in clinical and observational studies.Variable selection is an important issue in all regression analysis.In this paper, the authors first propose the estimation methods to select the significant variables,and then prove the asymptotic behavior of the proposed estimator.Furthermore,the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters.Finally,the finite sample estimation for the asymptotical covariance matrix is also proposed.     

An Additive-Multiplicative Cox-Aalen Subdistribution Hazard Model for Competing Risks Data

This paper proposes a flexible additive-multiplicative Cox-Aalen hazard model which allows time-varying covariate effects for the subdistribution in a competing risks study. Weighted estimating equation approaches under an covariates-dependent adjusted weight by fitting the Cox proportional hazard model for the censoring distribution are established for inference on the model parametric and nonparametric components. In addition, large number properties are presented and the finite sample behavior of the proposed estimators is evaluated through simulation studies, estimators from the proposed method perform satisfactorily on reduction of the bias. The authors apply our model to a competing risks data set from a tamoxifen trail for breast cancer study.     

Variable Selection of Varying Dispersion Student-t Regression Models

The Student-t regression model is a useful extension of the normal model,which can be used for statistical modeling of data sets involving errors with heavy tails and/or outliers and provides robust estimation of means and regression coefficients.In this paper,the varying dispersion Student-t regression model is discussed,in which both the mean and the dispersion depend upon explanatory variables.The problem of interest is simultaneously select significant variables both in mean and dispersion model.A unified procedure which can simultaneously select significant variable is given.With appropriate selection of the tuning parameters,the consistency and the oracle property of the regularized estimators are established.Both the simulation study and two real data examples are used to illustrate the proposed methodologies.     

基于规划识别的社会安全事件识别方法

社会安全事件发生之前通常要进行筹划、准备,并逐步成型,通过观察这一过程中参与者的行为和动作有可能识别正在酝酿的社会安全事件.一些社会安全事件的行动单独来看并不一定表现出异常特征,需要综合分析发现多个参与者行动相互关联后可能形成的事件.文章假设参与者在事件准备和形成过程中的部分行动是可以被观察到的,提出了基于规划识别的社会安全事件识别方法.首先构造了社会安全事件识别模型框架,描述了社会安全事件识别的领域知识和问题模型,进而采用plan recognition as planning (PRAP)规划识别方法,利用领域知识进行推理,动态地识别可能发生的社会安全事件.最后以人员聚集事件为例设计了实验算例,检验了方法的有效性.实验结果表明该方法能够准确识别参与者行动对应的社会安全事件,并且具备较强的提前识别能力,能够提高社会安全事件监测预警能力.     

事件影响下的时间序列多尺度集成预测

针对时间序列的非线性、非平稳和多尺度特征,考虑到事件对序列结构产生的影响,提出事件影响下的时间序列多尺度集成预测策略。首先,基于经验模态分解将原始序列分解成若干分量,从多个尺度展现序列的基本构成;随后,基于迭代累积平方和实现分量序列的变点检验,从多个尺度判别和获取事件对序列产生的结构性影响;然后,基于干预分析构建事件对不同分量序列的影响模型,据此剔除事件影响,获取净化序列;最后,运用基于粒子群优化的支持向量回归,建立单一尺度的序列预测模型,进而实现事件影响下的时间序列多尺度集成预测。实证分析表明:该策略能够精细辨识事件对序列的多尺度影响,有效建立序列总体及分量的预测模型,与传统方法相比,具有更强的事件辨识能力、自适应建模能力和更高的预测精度。     

基于跨媒体分析的突发事件检测及趋势研判研究

突发事件的发生对社会经济产生重要影响,如何实时检测突发事件并对事件发展进行追踪,为应急处置和应急管理争取有效处理时间并提供决策支持具有重要研究意义.本文提出了一个基于跨媒体分析的突发事件检测模型,并对检测到的突发事件进行趋势分析和追踪.该方法通过对跨媒体文本进行深入分析,利用文本挖掘得到突发事件特征,并利用数据挖掘方法进行建模,有效提取突发事件;进一步地,利用事件分析和预测方法,对事件的进一步发展进行追踪,预测突发事件的未来走势.实证结果表明,本文提出的模型具有较好的检测精度,是突发事件检测与分析的新方法,具有较好的应用价值.     

基于高频极值数据的波动率建模与预测

本文系统性地研究了三种类别的高频极值数据下波动率的建模与预测，即分别基于高频收盘价数据、高频高低价数据、以及高频"开高低收"数据，讨论并完善了相应的连续价格假设下的估计量和跳跃价格假设下的估计量的理论性质，并将基于高频极值数据的各类估计方法统一地扩展为相应的动态预测模型，通过对上证指数及其他几种主要指数的高频数据进行实证分析，揭示出充分地利用高频极值数据信息可以显著地提高波动率的模型拟合效果和样本外动态预测能力.     

基于大数据的食源性疾病事件探测与风险评估

食源性疾病由于其症状轻重不一常被低估,但近年来,食源性疾病的爆发在全国范围内呈上升趋势,准确探测食源性疾病事件并对其进行风险评估有重要意义.本文分别对哨点医院监测数据、食品检测数据和来自互联网的数据建立事件探测模型,实现风险评估,并分析比较模型优劣,最后建立统一的时空框架,引入人口、交通、食品生产等大数据对风险预测结果进行综合集成.通过对某大城市2014年食源性疾病事件的探测结果对比,实证结果表明,综合模型预测的时空精度更高,对防控更具操作性.     

Approximate conditional likelihood for generalized linear models with general missing data mechanism

The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing methods in the literature heavily depend on an unverifiable assumption of the missing data mechanism, and they fail when the assumption is violated. This paper proposes a missing data mechanism that is as generally applicable as possible, which includes both ignorable and nonignorable missing data cases, as well as both scenarios of missing values in response and covariate. Under this general missing data mechanism, the authors adopt an approximate conditional likelihood method to estimate unknown parameters. The authors rigorously establish the regularity conditions under which the unknown parameters are identifiable under the approximate conditional likelihood approach. For parameters that are identifiable, the authors prove the asymptotic normality of the estimators obtained by maximizing the approximate conditional likelihood. Some simulation studies are conducted to evaluate finite sample performance of the proposed estimators as well as estimators from some existing methods. Finally, the authors present a biomarker analysis in prostate cancer study to illustrate the proposed method.     

基于深度网络的蓝军装备模型参数生成方法研究

蓝军装备的建模仿真是构建对抗仿真环境不可缺少的部分。针对蓝方系统可获取的参数有限、具有“贫信息”“小样本”特性的问题,提出一种基于深度网络的蓝军装备模型参数生成方法。通过设定信息注入蓝军装备的仿真模型,生成仿真数据,利用数据训练深度神经网络。得到的网络对该型装备的未知参数预测具有一定的泛化能力,可直接用于预测或作为迁移学习的源模型。以蓝军某型拦截弹的建模仿真为例对该方法进行应用验证,使用了多层感知机和循环神经网络2种网络对比例导引系数进行学习与预测,均获得了较好的效果。     

基于事件B的处理器形式化建模方法的研究

针对当前可重定向的模拟器生成平台的需求和现有的处理器模型的缺陷,提出了一种基于事件B的处理器模型EBPM(EventBbasedProcessorModel)。利用事件B中的抽象系统,通过资源、指令和控制三个子模型从结构、行为和时序三方面对处理器进行建模,每个子模型中分别由抽象系统的状态和事件两部分刻画其静态行为和动态行为。该模型不但具有很强的灵活性和扩展性,还支持指令级和周期级两个层次的建模。通过对MIPS处理器的流水线进行形式化建模的实例说明了EBPM的有效性。     

连续/离散混合型制造系统的生产过程虚拟仿真建模

离散事件仿真建模被广泛应用于制造系统的设计及运行控制,但是很多制造系统兼有离散系统和连续系统的特点。提出一种将离散事件与连续系统结合起来的仿真建模方法,以支持动态展现这类制造系统的生产过程。该方法的主要思想是,主机内部的机构运动主要采用连续系统进行仿真建模,而主机之间的加工顺序则利用离散系统来构建其仿真模型,然后由统一的仿真管理器管理。论文详细描述了虚拟仿真引擎中的仿真时钟推动机制,并用这一方法建立了面向啤酒灌装生产线生产过程的虚拟仿真模型,说明了这一方法的可行性。     

集装箱码头物流运营系统通用性仿真建模共性抽象研究

共性抽象是通用性仿真建模的必经过程和基础。提出一种创新的共性抽象方法为基于离散事件动态系统的集装箱码头物流运营系统通用性仿真建模提供一个较为清晰的全貌、框架和体系。首先以辩识该系统仿真建模的关键性问题为突破,构建共性抽象的对象和核心元素;继而通过高度提炼和概括,抽象通用性的基本属性和特征,并建立相应的基于离散事件动态系统的人为规则;然后按照人为规则将共性抽象的基本元素抽象为逻辑元素和实体元素,并设计为相应的标准化图例集以系统地描述共性抽象,既可提供给程序设计人员对程序逻辑进行建模,也可提供给非程序设计人员对业务过程进行建模;最后通过对洋山深水港一期集装箱码头仿真实验,验证所提出的共性抽象方法的有效性。     

Sieve least squares estimator for partial linear models with current status data

Current status data often arise in survival analysis and reliability studies, when a continuous response is reduced to an indicator of whether the response is greater or less than an observed random threshold value. This article considers a partial linear model with current status data. A sieve least squares estimator is proposed to estimate both the regression parameters and the nonparametric function. This paper shows, under some mild condition, that the estimators are strong consistent. Moreover, the parameter estimators are normally distributed, while the nonparametric component achieves the optimal convergence rate. Simulation studies are carried out to investigate the performance of the proposed estimates. For illustration purposes, the method is applied to a real dataset from a study of the calcification of the hydrogel intraocular lenses, a complication of cataract treatment.     

基于图结构的暴雨事件组织方法研究

在高时空分辨率的降雨数据支持下,分析和追踪暴雨事件的精细结构和动态行为成为可能,但存在数据量大、数据组织结构复杂的特点,对暴雨事件的时空组织与存储提出了巨大挑战.本文基于暴雨事件的演化过程,设计了一种"顶点-边界"的图结构表达模型,并构建基于Neo4j的图数据库,实现暴雨事件的组织与存储.图数据结构中的顶点描述暴雨事件对象,记录暴雨事件空间位置、降雨数据等状态信息;图有向边对暴雨顶点进行关联,记录暴雨状态之间的关系、变化速度、方向等动态信息.最后,综合时间序列模型和空间拓扑关系,设计基于图的暴雨事件组织模型,基于Neo4j图存储方式R-Tree空间索引存储暴雨矢量数据集,并构建暴雨事件库.基于Neo4j和Oracle Spatial的暴雨事件库的对比分析结果表明,Neo4j事件库在数据入库和时空查询性能方面具有优势.     

模糊模型辨识中模糊聚类方法应用分析

模糊聚类算法已广泛应用于模式识别、数据聚类以及从数据中提取模糊规则的过程。介绍了基于模糊聚类的非线性系统模糊辨识方法 ,并通过著名的Box和Jenkins煤气炉数据仿真实例详细研究了模型性能指标与输入变量及模糊聚类数之间的关系 ,指出了应用模糊聚类方法的优势与不足。对于模糊建模中正确应用模糊聚类方法具有重要指导意义     

事件树建模及其在石化安全评估软件中的应用

在石油化工领域，有效地进行安全评估具有重要意义。事件树建模是进行系统安全评估的一种有效方法。本文从世界银行颁布的“工业风险评估技术标准”出发，采用事件树建模方法，开发了一套石油化工安全评估软件。本软件包括危险部件选取、事件树分析、模型算法库、结果报表、分析评估和帮助等六个模块，涵盖安全评估的全流程。在软件核心部分事件树分析模块中，针对石油化工领域内的四大主要危险发生源，建立了相应的四个事件树模型，分别为易燃气体事件树模型、易燃液体事件树模型、有毒气体事件树模型和有毒液体事件树模型。在模型算法库模块，给出了六类不同的模型算法子库，分别为泄漏模型子库、火灾模型子库、爆炸模型子库、扩散模型子库、毒云模型子库和溢流模型子库。另外，对软件中与建模相关的关键技术进行了详细讨论。     

A Constrained Interval-Valued Linear Regression Model: A New Heteroscedasticity Estimation Method

Linear regression models for interval-valued data have been widely studied. Most literatures are to split an interval into two real numbers, i.e., the left- and right-endpoints or the center and radius of this interval, and fit two separate real-valued or two dimension linear regression models. This paper is focused on the bias-corrected and heteroscedasticity-adjusted modeling by imposing order constraint to the endpoints of the response interval and weighted linear least squares with estimated covariance matrix, based on a generalized linear model for interval-valued data. A three step estimation method is proposed. Theoretical conclusions and numerical evaluations show that the proposed estimator has higher efficiency than previous estimators.

     

Variable selection in joint location,scale and skewness models of the skew-normal distribution

Variable selection is an important research topic in modern statistics, traditional variable selection methods can only select the mean model and (or) the variance model, and cannot be used to select the joint mean, variance and skewness models. In this paper, the authors propose the joint location, scale and skewness models when the data set under consideration involves asymmetric outcomes, and consider the problem of variable selection for our proposed models. Based on an efficient unified penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. The authors develop the variable selection procedure for the proposed joint models, which can efficiently simultaneously estimate and select important variables in location model, scale model and skewness model. Simulation studies and body mass index data analysis are presented to illustrate the proposed methods.