Multilevel models for repeated binary outcomes: attitudes and voting over the electoral cycle

Models for fitting longitudinal binary responses are explored by using a panel study of voting intentions. A standard multilevel repeated measures logistic model is shown to be inadequate owing to a substantial proportion of respondents who maintain a constant response over time. A multivariate binary response model is shown to be a better fit to the data.     

Influence of human immunodeficiency virus infection on neurological impairment: an analysis of longitudinal binary data with informative drop-out

A study to investigate the human immunodeficiency virus (HIV) status on the course of neurological impairment, conducted by the HIV Center at Columbia University, followed a cohort of HIV positive and negative gay men for 5 years and assessed the presence or absence of neurological impairment every 6 months. Almost half of the subjects dropped out before the end of the study for reasons that might have been related to the missing neurological data. We propose likelihood-based methods for analysing such binary longitudinal data under informative and non-informative drop-out. A transition model is assumed for the binary response, and several models for the drop-out processes are considered which are functions of the response variable (neurological impairment). The likelihood ratio test is used to compare models with informative and non-informative drop-out mechanisms. Using simulations, we investigate the percentage bias and mean-squared error (MSE) of the parameter estimates in the transition model under various assumptions for the drop-out. We find evidence for informative drop-out in the study, and we illustrate that the bias and MSE for the parameters of the transition model are not directly related to the observed drop-out or missing data rates. The effect of HIV status on the neurological impairment is found to be statistically significant under each of the models considered for the drop-out, although the regression coefficient may be biased in certain cases. The presence and relative magnitude of the bias depend on factors such as the probability of drop-out conditional on the presence of neurological impairment and the prevalence of neurological impairment in the population under study.     

The Effect of Drop-Out on the Efficiency of Longitudinal Experiments

It is shown that drop-out often reduces the efficiency of longitudinal experiments considerably. In the framework of linear mixed models, a general, computationally simple method is provided, for designing longitudinal studies when drop-out is to be expected, such that there is little risk of large losses of efficiency due to the missing data. All the results are extensively illustrated using data from a randomized experiment with rats.     

Semiparametric Additive Accelerated Life Models

In this paper we investigate the asymptotic properties of estimators obtained for the semiparametric additive accelerated life model proposed by Bagdonavicius & Nikulin (1995). This model generalizes the well known additive hazards model of survival analysis and is close to the general transformation model (see Dabrowska & Doksum, 1988). Asymptotic properties of the estimator of the regression parameter and the estimator of the reliability function are given in the case of right censoring for discretized data and a numerical example illustrates these results.     

Kernel Density Estimation with Generalized Binning

We propose kernel density estimators based on prebinned data. We use generalized binning schemes based on the quantiles points of a certain auxiliary distribution function. Therein the uniform distribution corresponds to usual binning. The statistical accuracy of the resulting kernel estimators is studied, i.e. we derive mean squared error results for the closeness of these estimators to both the true function and the kernel estimator based on the original data set. Our results show the influence of the choice of the auxiliary density on the binned kernel estimators and they reveal that non-uniform binning can be worthwhile.     

网络环境下图书馆采访工作初探

分析了网络环境给图书馆工作所带来的机遇和优势 ,认为它不仅使图书馆的采购方式得到了优化 ,而且也大大提高了购书质量和采购效率 ,节约了大量资金。据此 ,提出面对新的形势发展 ,应该通过强调提高采访人员素质、实现资源共享、建立文献出版信息数据库及相关信息库、合理选择各类信息资源等措施来进一步加强图书馆的采访工作 ,以便真正提升建立高质量馆藏所产生的实际应用价值。     

智能网联汽车体系结构与关键技术

智能网联汽车（Intelligent connected vehicles,ICV）是无人驾驶技术的核心载体,产业链长,市场巨大,吸引了国内外众多汽车厂商和信息企业都纷纷投向ICV的研发与测试,部分ICV车型已进入量产阶段,新的概念车型也陆续提出。针对新型ICV的发展现状与关键技术,从ICV智能化和网联化两个维度对ICV中涉及到的相关技术进行分析,进一步从信息处理的感知层、决策层、控制层3个层次构建了ICV发展过程中的关键技术体系结构。研究表明,车辆环境感知和通信技术技术涵盖自车状态和外部环境,短程通信技术（DSRC）、机器视觉和激光雷达技术是实现车辆环境信息采集功能完善的关键;车路协同系统涉及车—车通信、车—路通信、车—云通信,协同智能交通系统、云平台和大数据技术是车路协同技术的关键;驾驶辅助技术包括自适应巡航控制系统、车道偏离/避免系统、碰撞预警/避免系统等8个构成,这是辅助驾驶、部分自动驾驶和有条件的自动驾驶3个智能化阶段中的关键;具有自主学习、自我决策的人工智能技术和信息安全技术也是智能网联汽车的关键技术,在此基础上,还应该实现多种技术间的相互支撑与融合,克服关键技术协同障碍,才能实现ICV的市场推广与普及应用。     

一种冲突证据的融合方法

针对D-S证据理论的冲突证据融合问题进行了研究,通过对证据进行冲突检验及修正弥补了D-S证据理论在处理冲突证据时的不足。采用了加性策略对事件进行排序的方法,找出证据支持最大的q个事件,就是最有可能是识别结论的q个事件,对其进行冲突检验及修正。解决了D-S证据合成方法组合冲突证据时出现的问题,保证了非冲突证据的有效合成。通过仿真试验表明该算法能有效地排除干扰,得出了符合实际的组合结果。     

共享价值观：大数据时代的信息伦理精神

大数据时代,数据即共享,建立在群体理性和契约精神基础之上的共享价值观逐渐成为大数据时代的主要伦理精神。大数据时代共享价值观的主要伦理效应在于解决信息不对称的问题,同时破除因信息壁垒阻隔而形成的“信息孤岛”现象,促进信息的共享流转和价值转化。大数据时代,共享价值观面临着信息的高成本获取与低成本共享之间的矛盾、共享数据被污染的潜在风险,以及信息共享行为强化了媒介的“反向驯化”、“数据鸿沟”加剧了信息传播的不平等挑战。践行共享价值观,需要把握信息获取者与共享者之间的权利“平衡点”,强化社会监督与法治,以确保信息的科学和准确;充分挖掘智能技术在信息伦理建设方面的作用,以避免数据“过娱乐化”陷阱;注重共享的广泛性与公平性,以促进信息与数据负责任、有价值的共享。