Caregivers' perspectives on the SafeCare® programme: Implementing an evidence‐based intervention for child neglect

This qualitative study examined caregivers' experiences with SafeCare®, an evidence‐based programme that focuses on child neglect through modules on health, safety, and parenting. Shortly after completing SafeCare, 30 caregivers participated in a semi‐structured interview about their experiences with the programme. Overall, caregivers indicated that the programme helped with improvements in their parenting skills. Among the factors that contributed to a positive experience were the simplicity of language, the skills‐based approach, and the quality of the relationship with the SafeCare provider. Caregivers also noted several factors that made it difficult to fully benefit from the programme, including financial constraints, removal of their child from the home, and general distrust towards the child welfare system. Findings provide relevant information for SafeCare providers in terms of identifying areas that work well for caregivers completing the programme, as well as areas that might serve as impediments. Implications for contemporary child welfare practice are also considered.     

Reliability of global sensitivity indices

Uncertainty and sensitivity analysis is an essential ingredient of model development and applications. For many uncertainty and sensitivity analysis techniques, sensitivity indices are calculated based on a relatively large sample to measure the importance of parameters in their contributions to uncertainties in model outputs. To statistically compare their importance, it is necessary that uncertainty and sensitivity analysis techniques provide standard errors of estimated sensitivity indices. In this paper, a delta method is used to analytically approximate standard errors of estimated sensitivity indices for a popular sensitivity analysis method, the Fourier amplitude sensitivity test (FAST). Standard errors estimated based on the delta method were compared with those estimated based on 20 sample replicates. We found that the delta method can provide a good approximation for the standard errors of both first-order and higher-order sensitivity indices. Finally, based on the standard error approximation, we also proposed a method to determine a minimum sample size to achieve the desired estimation precision for a specified sensitivity index. The standard error estimation method presented in this paper can make the FAST analysis computationally much more efficient for complex models.     

中美利差对人民币汇率变动的影响分析

以利差对汇率传导机制和路径的理论分析为基础，通过建立汇率行为路径分析模型，检验有关名义利差与名义汇率关系的理论和经验方面的问题。中美实证研究结果表明，名义利差变动对名义汇率变动的影响支持利率平价理论，但利差对汇率变动的传导路径较利率平价理论更为复杂，且传导方式和效应大小在2005年7月汇改前后有显著差别。     

Statistical properties of detrended fluctuation analysis

The main goal of this work is to consider the detrended fluctuation analysis (DFA), proposed by Peng et al. [Mosaic organization of DNA nucleotides, Phys. Rev. E. 49(5) (1994), 1685–1689]. This is a well-known method for analysing the long-range dependence in non-stationary time series. Here we describe the DFA method and we prove its consistency and its exact distribution, based on the usual i.i.d. assumption, as an estimator for the fractional parameter d. In the literature it is well established that the nucleotide sequences present long-range dependence property. In this work, we analyse the long dependence property in view of the autoregressive moving average fractionally integrated ARFIMA(p, d, q) processes through the analysis of four nucleotide sequences. For estimating the fractional parameter d we consider the semiparametric regression method based on the periodogram function, in both classical and robust versions; the semiparametric R/S(n) method, proposed by Hurst [Long term storage in reservoirs, Trans. Am. Soc. Civil Eng. 116 (1986), 770–779] and the maximum likelihood method (see [R. Fox and M.S. Taqqu, Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series, Ann. Statist. 14 (1986), 517–532]), by considering the approximation suggested by Whittle [Hypothesis Testing in Time Series Analysis (1953), Hafner, New York].     

古汉语语法研究的创新分析与朴实文风——袁本良《守拙斋汉语史论稿》语法论文研究

文章论述袁本良古汉语语法研究的创新分析与朴实文风：一、传统经典语法著作的现代意义＂新＂分析;二、古汉语语法专题的另视角＂新＂分析;三、语料翔实,继承朴学传统;文风朴实,显现学者风范。     

对外贸易对河南省GDP贡献的实证分析

对1985-2007年有关贸易数据的计量分析表明,河南的对外贸易对本省GDP的增长具有较大的拉动力,但出口产品结构层次不高等问题导致河南对外贸易效益低下。改变现有的贸易方式,提高河南经济的国际竞争力,应当从加快转变贸易增长方式、扩大自有品牌产品出口、大力发展加工贸易、适度扩大进口、鼓励境外投资等方面着手。     

汇率变动对日本贸易收支影响的实证研究

通过汇率变动对日本贸易收支影响的协整分析,发现实际有效汇率升值会引起日本的贸易顺差减少,但是汇率升值对日本贸易收支的影响不是十分显著,贸易收支的变动还要受到其他各种因素的制约。     

聚类分析法在高职院校订单式培养学生综合素质评价中的应用

订单式”人才培养模式的建立为高职教育教学改革注入了新的活力。但是，由高职院校因为缺乏有效的培养过程控制与竞争机制，也出现了影响该模式的良性循环和可持续发展的诸多问题。鉴于此，应尽快完善“订单式”人才培养模式中的过程控制与考核机制，建立一套科学、完备的考核指标体系是当务之急。对于订单式培养学生的综合素质评估结果，可以通过相关聚类分析方法，对订单班学生综合素质评估结果进行科学合理的分类，建立完善订单班学生能进能出、滚动淘汰的动态管理模式，达到订单式培养不仅“定数量”，更注重“定质量”的理想状态。     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

Bayesian discrete latent spatial modeling of crack initiation in orthopaedic hip replacement bone cement

In this paper, we propose a spatial model for the initiation of cracks in the bone cement of hip replacement specimens. The failure of hip replacements can be attributed mainly to damage accumulation, consisting of crack initiation and growth, occurring in the cement mantle that interlocks the hip prosthesis and the femur bone. Since crack initiation is an important factor in determining the lifetime of a replacement, the understanding of the reasons for crack initiation is vital in attempting to prolong the life of the hip replacement. The data consist of crack location coordinates from five laboratory experimental models, together with stress measurements. It is known that stress plays a major role in the initiation of cracks, and it is also known that other unmeasurable factors such as air bubbles (pores) in the cement mantle are also influential. We propose an identity-link spatial Poisson regression model for the counts of cracks in discrete regions of the cement, incorporating both the measured (stress), and through a latent process, any unmeasured factors (possibly pores) that may be influential. All analysis is carried out in a Bayesian framework, allowing for the inclusion of prior information obtained from engineers, and parameter estimation for the model is done via Markov chain Monte Carlo techniques.