Adaptive modeling of the human-environment relationship applied to estimation of the population carrying capacity in an earthquake zone

Natural catastrophes such as earthquakes can, in addition to causing loss of life, disrupt the urbanization process through the need for forced population redistribution and the modification of resource and environmental carrying capacity. The population carrying capacity (PCC) of an altered environment following an earthquake is a crucial determinant in the relocation of displaced persons. We use data adaptive methods to model the correlation between the physical environment and human population density in estimating PCC in areas affected by the 2008 Wenchuan earthquake. Comparing actual population distributions with ideal population distributions allows for the identification of villages where population exceeds PCC, or conversely, areas where the environment can support a higher population. Such a comparison can provide the basis for a relocation plan, a critical element of post-catastrophe policy-making.     

施工企业投标优化的问题与对策

通过对某大型施工企业多个铁路、公路施工项目部的调查，总结了企业当前投标优化方面存在的问题。针对企业以铁路、公路项目为主业的特点，提出了优势项目滚动发展、建立有效激励制度、做好投标准备、优化施工组织设计、应用报价技巧等优化对策。对于施工企业改善市场经营效果，提高中标质量，增加项目盈利有重要的借鉴作用。     

The Longitudinal Relationships Between Basic Psychological Needs Satisfaction at School and School-Related Subjective Well-Being in Adolescents

Situated within a positive psychology perspective, this study aimed at examining the nature and directionality of longitudinal relationships between basic psychological needs satisfaction at school and adolescents’ school-related subjective well-being. A total of 576 students (40.5 male and 36.8 % students in junior high school) completed measures of adolescent students’ basic psychological needs at school and school-related subjective well-being at two time points, 6 weeks apart. Cross-lagged structural equation modeling showed significant bidirectional longitudinal relationships between autonomy, relatedness, and competence needs satisfaction and school satisfaction. Furthermore, significant bidirectional relationships were observed between competence need satisfaction and positive affect in school. The findings provided important evidence of the roles of adolescents’ different types of needs satisfaction, experienced specifically during school, in adolescents’ school-related subjective well-being. The findings also helped extend the positive psychology perspective to the relatively neglected context of education.     

Embodied versus Disembodied Information: How Online Artifacts Influence Offline Interpersonal Interactions

This article examines how the sheer volume of personal information recorded and searchable online (online artifacts) has transformed the situated activity system central to Goffman's dramaturgical theories. In‐depth interviews reveal that individuals believe disembodied information based on online artifacts is a more accurate representation of others than embodied information from spatially and temporally bounded face‐to‐face (FTF) processes because they represent how others have behaved over time and are attested by their online contacts. However, the n‐adic structure of online interaction leads to mismatched expectations about whether disembodied information is taken into account during FTF encounters, and consequently can result in embarrassment.     

金融资产的动态非线性相关:一个新的模型

基于Kendall’sτ秩相关系数的优越性和定义,本文提出了新的具有明确经济意义的动态条件相关copula模型,将常用的Gaussian、Clayton和Gumbel函数统一根据该演化方程实现动态化,构造出三种Kendall’sτ动态条件相关copula模型,可用于刻画不同的相关模式。这些模型不仅参数少、容易估计,避免了现有动态条件相关copula模型构建方法各异导致的在实证中不利于比较的缺点,而且能够进行多步向前预测,有效地减少了进行样本外预测时的计算量,从而为刻画时变、非线性、非对称性和尾部相关等复杂的动态相关模式提供了新方法。     

Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation

Bridge penalized regression has many desirable statistical properties such as unbiasedness, sparseness as well as ‘oracle’. In Bayesian framework, bridge regularized penalty can be implemented based on generalized Gaussian distribution (GGD) prior. In this paper, we incorporate Bayesian bridge-randomized penalty and its adaptive version into the quantile regression (QR) models with autoregressive perturbations to conduct Bayesian penalization estimation. Employing the working likelihood of the asymmetric Laplace distribution (ALD) perturbations, the Bayesian joint hierarchical models are established. Based on the mixture representations of the ALD and generalized Gaussian distribution (GGD) priors of coefficients, the hybrid algorithms based on Gibbs sampler and Metropolis-Hasting sampler are provided to conduct fully Bayesian posterior estimation. Finally, the proposed Bayesian procedures are illustrated by some simulation examples and applied to a real data application of the electricity consumption.     

Diagnostics for quasi-likelihood nonlinear models

Quasi-likelihood nonlinear models (QLNMs) are an extension of generalized linear model and include a widen class of models as special cases. This article investigates some diagnostic methods in QLNMs. An equivalency between a case-deletion model and a mean-shift outlier model in QLNM is established. Two simulation study and a real dataset are used to illustrate the proposed diagnostic methods.     

多重假设检验中参数估计问题研究

在多重假设检验中,真正原假设的个数m0是未知的,但是它有着很重要的影响,因此,它在最近的统计文献中备受关注.文章综述了三种主要的估计方法:最低斜率法、三次样条法、均值估计方法.然后将上述三种方法结合起来,提出了新的估计方法:均值三次样条法,并主要研究了其在微阵列数据上的应用.大量的模拟研究表明,和其他方法相比,新的估计方法具有较小的偏差和标准差.最后利用真实数据来对估计方法进行评估,并找出了差异表达性基因.模拟和实际数据表明此方法具有显著性提高.     

长安出土《统毗伽可贺敦延陁墓志》考释

《统毗伽可贺敦延陁墓志》出土于长安昭陵,为李思摩妻子延陁氏的墓志.延陁氏乃薛延陀部族酋长之胄,后嫁于突厥可汗所出阿史那思摩.630年,延陁氏随思摩降唐,被安置在夏州一带,在故定襄城、长安等处亦有居所.作为突厥可汗的夫人,延陁氏亲历了隋末至唐初漠北地区的风云变幻,见证了突厥汗国和薛延陀汗国兴衰更替,这些内容在《统毗伽可贺敦延陁墓志》中多有反映,可与《李思摩墓志》相表里.该墓志为深入研究薛延陀部族的历史和贞观时期的北疆民族关系提供了重要的新证据.     

Composite quantile regression for massive datasets

Analysis of massive datasets is challenging owing to limitations of computer primary memory. Composite quantile regression (CQR) is a robust and efficient estimation method. In this paper, we extend CQR to massive datasets and propose a divide-and-conquer CQR method. The basic idea is to split the entire dataset into several blocks, applying the CQR method for data in each block, and finally combining these regression results via weighted average. The proposed approach significantly reduces the required amount of primary memory, and the resulting estimate will be as efficient as if the entire data set is analysed simultaneously. Moreover, to improve the efficiency of CQR, we propose a weighted CQR estimation approach. To achieve sparsity with high-dimensional covariates, we develop a variable selection procedure to select significant parametric components and prove the method possessing the oracle property. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.