城市老旧小区综合治理问题与对策的实证研究

为解决城市老旧小区在提升改造、居民自治、网格化管理、卫生环境治理和项目化服务运作等方面存在的问题,以西安市碑林区为案例,通过问卷调查、实地走访及对比国内外同类城市老旧小区治理等方法,对老旧小区综合治理问题进行了系统研究。研究认为,老旧小区房屋建筑与基础设施老化现象严重,乱搭乱建、脏乱差现象不同程度地存在;社会组织、市场力量、小区居民和社会志愿者等主体参与不足,现代物业管理服务严重缺乏,多元主体参与老旧小区工作的治理格局尚未形成;社会各界对老旧小区治理的民生意义与公益性定位还不明确,导致政府政策指导相对滞后,硬件提升改造与软环境治理方面的财政资金投入不足,人文关怀相对缺乏等。研究认为,老旧小区的综合治理应以公共服务理论和以人为本理论为支撑,以居民需求为出发点,以多元主体参与共治为着力点,以提高认识站位、完善支持性政策、加大资金投入、健全管理体系与工作机制为重要抓手,提升老旧小区的综合治理水平。     

高校档案信息资源建设与服务的需求主体分析

高校档案信息资源非常丰富,特色鲜明。根据高校档案需求主体的构成及不同需求主体的需求内容、需求特性、需求层次等方面的特征,将高校档案信息资源进行有效整合,并建设性地开发利用,以满足主体需求,对服务于高校教学与科学研究,提高高校管理效益,有重要意义。     

Testing for variance changes in autoregressive models with unknown order

The problem of change point in autoregressive process is studied in this article. We propose a Bayesian information criterion-iterated cumulative sums of squares algorithm to detect the variance changes in an autoregressive series with unknown order. Simulation results and two examples are presented, where it is shown to have good performances when the sample size is relatively small.     

Missing data techniques for multilevel data: implications of model misspecification

When modeling multilevel data, it is important to accurately represent the interdependence of observations within clusters. Ignoring data clustering may result in parameter misestimation. However, it is not well established to what degree parameter estimates are affected by model misspecification when applying missing data techniques (MDTs) to incomplete multilevel data. We compare the performance of three MDTs with incomplete hierarchical data. We consider the impact of imputation model misspecification on the quality of parameter estimates by employing multiple imputation under assumptions of a normal model (MI/NM) with two-level cross-sectional data when values are missing at random on the dependent variable at rates of 10%, 30%, and 50%. Five criteria are used to compare estimates from MI/NM to estimates from MI assuming a linear mixed model (MI/LMM) and maximum likelihood estimation to the same incomplete data sets. With 10% missing data (MD), techniques performed similarly for fixed-effects estimates, but variance components were biased with MI/NM. Effects of model misspecification worsened at higher rates of MD, with the hierarchical structure of the data markedly underrepresented by biased variance component estimates. MI/LMM and maximum likelihood provided generally accurate and unbiased parameter estimates but performance was negatively affected by increased rates of MD.     

A predictive approach to measuring the strength of statistical evidence for single and multiple comparisons

The normalized maximum likelihood (NML) is a recent penalized likelihood that has properties that justify defining the amount of discrimination information (DI) in the data supporting an alternative hypothesis over a null hypothesis as the logarithm of an NML ratio, namely, the alternative hypothesis NML divided by the null hypothesis NML. The resulting DI, like the Bayes factor but unlike the P‐value, measures the strength of evidence for an alternative hypothesis over a null hypothesis such that the probability of misleading evidence vanishes asymptotically under weak regularity conditions and such that evidence can support a simple null hypothesis. Instead of requiring a prior distribution, the DI satisfies a worst‐case minimax prediction criterion. Replacing a (possibly pseudo‐) likelihood function with its weighted counterpart extends the scope of the DI to models for which the unweighted NML is undefined. The likelihood weights leverage side information, either in data associated with comparisons other than the comparison at hand or in the parameter value of a simple null hypothesis. Two case studies, one involving multiple populations and the other involving multiple biological features, indicate that the DI is robust to the type of side information used when that information is assigned the weight of a single observation. Such robustness suggests that very little adjustment for multiple comparisons is warranted if the sample size is at least moderate. The Canadian Journal of Statistics 39: 610–631; 2011. © 2011 Statistical Society of Canada     

不同宗教信仰间的调适与共存——一个贡山怒族（阿怒）实例的文化解读

贡山怒族（阿怒）社会至今已形成了在同一个社会、村落,甚至同一个家庭中藏传佛教①、天主教、基督教②等多种宗教信仰共存的事实。形成和维持这一状态的因素是多方面的,包括其特殊的历史机缘及政治、经济等外部动因,而以阿怒文化,特别是原生信仰为认知模式的对各种外来宗教在其生计模式、文化系统、社会系统及社会生活中的动态调适则是多种不同宗教信仰共存的基础。     

交织多址接入系统信道容量证明

交织多址接入系统(IDMA)是使用低速率码并利用交织器区分用户的多址接入系统,被认为是码分多址(CDMA)的特例。IDMA省去专门的扩频序列,通过迭代检测提高系统性能,继承了CDMA抗多径衰落和抗多用户干扰等特性,但对于IDMA和CDMA在同样的信道条件下具有相同的信道容量这一基本问题没有给出严格的理论说明。该文在给出交织多址接入系统码定义的基础上,引入了信息论中的平均互信息量作为度量信道容限的标准,研究了K个用户的IDMA系统信道容限问题,得到了与CDMA系统相同的信道容限,并用随机编码和联合典型列的方法对结论进行了证明。由此从信息论的角度证明了IDMA是CDMA的特例,为IDMA的研究工作完成信息论方面的基础证明。     

30年改革开放：天津商业发展管窥

改革开放30年来,我市商业系统通过体制的变迁和改革,促使市场多元主体形成,推动了流通规模持续扩大、现代化程度日益提高和商业功能的有效拓展。天津商业要在实现科学发展、和谐发展、率先发展中有所作为有所贡献,必须以取得的成就基础上进一步深化改革,扩大开放,统筹协调天津商业发展。     

对方案有偏好的区间数多属性灰色关联决策模型

针对属性值以区间数形式给出并且已知方案偏好信息的多属性决策问题,提出了一种灰色关联分析的决策方法。该方法依据一般的灰色关联分析方法的基本思路,给出了该问题的计算步骤,其核心是通过构建并求解一个单目标最优化模型,得到属性权重信息,从而计算出每个方案客观偏好值与主观偏好值的灰色关联系数,进而得到每个方案客观偏好与主观偏好的关联度,根据关联度对所有方案进行排序。最后给出了一个数值例子,结果表明方法简单,有效和易于计算。     

Market‐Based Allocation with Indivisible Bids

We study multiunit double auctions accepting bids with indivisibility constraints. Modeling the auction problem as a Multiple Choice Knapsack Problem and using dynamic programming, we show that incremental computations during bid processing can speed the handling of key auction operations such as clearing and quoting. We propose different price‐quote policies and study their influence on the efficiency of market‐based allocation. Using a reconfigurable manufacturing scenario where agents trade large quantities of multiple goods, we demonstrate potential benefits of supporting indivisibility constraints in bidding. These benefits are highly sensitive to the form of price quote provided, indicating interesting tradeoffs in communication and allocation efficiency.