区域碳锁定的判定研究--以云南省为例

通过建立包括陆地、湿地和海洋的碳承载力、碳超载率的计算模型，采用供需平衡法建立了区域碳锁定时间和趋势的判定模型。以有着“植物王国”和“基因宝库”之称以及水电资源相对丰富的云南省为例，对该省的碳锁定时间、趋势进行了实证研究。实证分析结果显示，云南省自2002年后开始出现了碳锁定现象且趋势越来越严重，若不进行强有力的外界干扰，这种趋势将一直延续下去。结合即使“十一·五”低碳目标的完成也没能减轻云南省碳锁定加剧的趋势，提出把减轻碳超载、控制能源消费总量、提升碳承载力纳入低碳经济发展的目标管理。     

The Common Assessment Framework form 9 years on: a creative process

Legislation within England states that local authorities should provide services for all those families in need. However, research has identified that regardless of the introduction of strategies to identify need and enhance family support, ongoing barriers to services adhere. Taking a social constructionist approach, this study explored professionals' experiences of the use of the Common Assessment Framework form. Data were collected in four different local authorities in two phases. Forty‐one professionals from a variety of agencies took part in semi‐structured interviews. Data were analyzed thematically. Findings demonstrate that the professionals experienced difficulties in working through the Common Assessment Framework process, for example, in completing the form and engaging families. This situation led to the more experienced and knowledgeable professionals utilising creative ways to successfully navigate the ‘referral process’. Such creative working practices included the terminology used to complete the form and how the process was ‘sold’ to parents, so that they could be in a better position to engage parents and complete the Common Assessment Framework form. Because of this, more experienced professionals seem to be able to accelerate the referral process in order to access much needed support services for children and young people.     

Extending social security coverage to the rural sector in China

In late 2009 China launched an innovative, voluntary programme that by 2011 had extended pension coverage to 326.4 million people in the rural sector, including contributors and beneficiaries. It requires one contribution per year and provides a flat‐rate benefit and a contributions‐related benefit through a contributory individual account, with a government guarantee that the benefit will continue for life. The programme encourages participation of persons who do not pay income taxes, and thus have no tax incentive to participate, by providing substantial government subsidies. As a further incentive, old‐age benefits are provided to older parents when all their adult children participate in the contributory programme.     

昌北高校图书馆联盟虚拟参考咨询服务现状分析

高校图书馆联合虚拟参考咨询服务是一种全新的信息服务形式,深受广大用户的喜爱和欢迎。该文通过对江西省昌北高校图书馆联盟2011年1至6月份虚拟参考咨询的服务人员、服务形式、服务内容、服务效率及存在问题进行调查分析研究,提出提升该联盟虚拟参考咨询服务质量的积极建议及对策。     

中国拆船业的发展对策

随着国际拆船业持续繁荣,拆船大国之间的竞争越来越激烈。如何促进中国拆船业的发展,提升其国际竞争力成为亟待解决的问题。首先阐述中国拆船业的发展历程,然后对影响中国拆船业发展的因素进行分析,并运用状态空间模型预测未来两年世界和中国拆船业的发展状况,进而提出促进中国拆船业健康发展的相关建议。     

Books on probability ideas are reviewed

In this paper, semiparametric methods are applied to estimate multivariate volatility functions, using a residual approach as in [J. Fan and Q. Yao, Efficient estimation of conditional variance functions in stochastic regression, Biometrika 85 (1998), pp. 645–660; F.A. Ziegelmann, Nonparametric estimation of volatility functions: The local exponential estimator, Econometric Theory 18 (2002), pp. 985–991; F.A. Ziegelmann, A local linear least-absolute-deviations estimator of volatility, Comm. Statist. Simulation Comput. 37 (2008), pp. 1543–1564], among others. Our main goal here is two-fold: (1) describe and implement a number of semiparametric models, such as additive, single-index and (adaptive) functional-coefficient, in volatility estimation, all motivated as alternatives to deal with the curse of dimensionality present in fully nonparametric models; and (2) propose the use of a variation of the traditional cross-validation method to deal with model choice in the class of adaptive functional-coefficient models, choosing simultaneously the bandwidth, the number of covariates in the model and also the single-index smoothing variable. The modified cross-validation algorithm is able to tackle the computational burden caused by the model complexity, providing an important tool in semiparametric volatility estimation. We briefly discuss model identifiability when estimating volatility as well as nonnegativity of the resulting estimators. Furthermore, Monte Carlo simulations for several underlying generating models are implemented and applications to real data are provided.     

The bumpy road to ‘becoming’: capturing the stories that teenage mothers told about their journey into motherhood

Understanding the experience of women who become mothers during their teenage years is central to ensuring that the support that is offered is appropriate to meet their needs. This paper reports on a small part of a larger ethnographic study that captured the lived experience of young mothers who were between the ages of 16 and 19 years that potentially typifies and illuminates the experiences of young women who become mothers in their teenage years. By collecting data from narrative interviews as well as participant and non‐participant observations over an extended period of time it was possible to identify how the young women experienced a range of difficulties as they made their transition into motherhood. Drawing on the findings, this paper argues that this transition for teenage mothers can be significantly different from the experience of older mothers, and it identifies the importance of appropriate support to mediate the challenges that they face. Understanding the young women's journey to ‘becoming’ is critical when planning services because if their experience of support is negative, it can lead to increased levels of maternal stress and reluctance to engage with support services.     

Quantile regression and variable selection for the single-index model

In this paper, we propose a new full iteration estimation method for quantile regression (QR) of the single-index model (SIM). The asymptotic properties of the proposed estimator are derived. Furthermore, we propose a variable selection procedure for the QR of SIM by combining the estimation method with the adaptive LASSO penalized method to get sparse estimation of the index parameter. The oracle properties of the variable selection method are established. Simulations with various non-normal errors are conducted to demonstrate the finite sample performance of the estimation method and the variable selection procedure. Furthermore, we illustrate the proposed method by analyzing a real data set.     

Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose‐response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the “hybrid” method proposed by Crump, two strategies of BMA, including both “maximum likelihood estimation based” and “Markov Chain Monte Carlo based” methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose‐response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose‐response data.     

An Additive–Multiplicative Restricted Mean Residual Life Model

The mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an additive–multiplicative restricted mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline. For the suggested model, some covariate effects are allowed to be time‐varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial.