地震后家庭幸福指数研究

2008年5月12日，举国震动的汶川大地震打破了平静，灾区人民的生命财产遭受巨大损失。灾后重建，八方支援，灾区人民找回失去的幸福。文章采用问卷调查地震灾区重建后家庭幸福感状况，从物质生活幸福感、环境幸福感和精神生活幸福感三因子反映地震后家庭幸福指数，构建因子模型，以期向爱心人士提供灾后重建的家庭幸福感的可靠信息。     

伸展构造背景下倾角资料的构造解释

系统总结了在伸展构造背景下，导致反映构造特征的地层倾角模式复杂化的多种因素。沉积相的不同、岩性变化、井眼状况和测井作业时间等多种因素都可能使地层倾角资料复杂化。进一步指出在拉张盆地中，进行倾角资料的构造解释，必须了解其构造样式的多样性和复杂性，解释存在的多解性。重点解剖了铲式断层在横剖面情况下，对于不同构造位置下的倾角模式。详细分析了一个倾角模式对应多种构造样式的倾角构造解释实例。倾角资料的构造解释应是综合解释，既要综合井筒的相关地质资料，又要综合测井与地质知识，还必须考虑综合地震资料。     

青岛市乡村休闲旅游 SWOT 分析研究

运用SWOT 分析法对青岛市乡村休闲旅游发展进行分析，结果显示：青岛具有资源丰富、经济发达、交通便利等优势，但同时也存在认识水平不高、农村基础设施落后、产品同质化严重、季节性明显、人才缺乏等劣势；面临旅游新热点形成、政府出台鼓励政策、新型农村社区的建设等新的发展机遇，但周边地区的竞争、生态环境的恶化也对其构成了威胁。在此基础上，提出了提高认识水平、完善基础设施、打造特色品牌、保护资源、培养人才等可行性建议。     

海峡两岸休闲农业合作相关主体行为的博弈分析

随着海峡两岸休闲农业合作的逐步发展，各相关主体之间的博弈也逐渐显现。在此过程中，涉及的主要行为主体有地方政府、农户以及台商企业。文章从各主体的利益行为分析出发，构建并分析政府与台商企业、台商企业与农户之间的动态博弈。分析表明：在博弈的过程中，政府处于主动和有利的地位，但其存在不规范行为；台商企业基于趋利的本性，在与政府之间的博弈会展开“寻租”行为，在与农户之间的博弈以牺牲农户的利益来实现其收益最大化；农户与台商企业的博弈相对处于弱势地位，加之其有限理性和知识能力有限，自身利益难以得到保障；为实现各主体利益最大化的可持续发展，应建立健全相关法律法规；完善政府的服务职能；构建合理的利益分配形式。     

Indigenous data,indigenous methodologies and indigenous data sovereignty

The field of Indigenous methodologies has grown strongly since Tuhiwai Smith’s 1999 groundbreaking book Decolonizing Indigenous Methodologies. For the most part however, there has been a marked absence of quantitative methodologies with the methods aligned with Indigenous methodologies predominantly qualitative. This article proposes that the absence of an Indigenous presence from Indigenous data production has resulted in an overwhelming statistical narrative of deficit for dispossessed Indigenous peoples around the globe. Using the theoretical concept of Indigenous Lifeworlds this article builds on the core premises of Walter and Andersen’s 2013 book Indigenous quantitative methodologies. Arguing for a fundamental disturbance of the Western logics of statistical data the article details recent developments in the field including the emergence of the Indigenous Data Sovereignty movement. The article also explores Indigenous quantitative methodologies in practice using the case study of a Tribal Epidemiology Centre in New Mexico.     

Risk factors for child maltreatment in South Korea: An investigation of a nationally representative sample

This study investigated the risk factors associated with the occurrence of child physical and psychological abuse in South Korea based on the ecological theory of child maltreatment. A subsample of 3‐ to 18‐year‐old children from “A Study on the Current State of Child Abuse and Neglect,” a nationally representative study on child abuse and neglect, was utilized for secondary data analysis. The sample was divided into two age groups (third graders in elementary school and below and fourth graders and above). We utilized hierarchical logistic regression for each age group separately to analyze the data. The variables at each level of the system (i.e., ontogenic development, microsystem, and exosystem) were entered into the model in sequential order. The results showed the common risk factors for both age groups are the caregiver's experience of abuse in childhood, the child's problem behavior, exposure to domestic violence, community size, and informal social control. The child's age was also significant in both groups but in the opposite direction. Social support was a significant predictor for the younger age group only, while the caregiver's level of education was significant for the older age group only. Implications for future research and practice are discussed based on the study results.     

Designing centralized waiting lists for attachment to a primary care provider: Considerations from a logic analysis

Access to a regular primary care provider is essential to quality care. In Canada, where 15 % of patients are unattached (i.e., without a regular provider), centralized waiting lists (CWLs) help attach patients to a primary care provider (family physician or nurse practitioner). Previous studies reveal mechanisms needed for CWLs to work, but focus mostly on CWLs for specialized health care. We aim to better understand how to design CWLs for unattached patients in primary care.In this study, a logic analysis compares empirical evidence from a qualitative case study of CWLs for unattached patients in seven Canadian provinces to programme theory derived from a realist review on CWLs. Data is analyzed using context-intervention-mechanism-outcome configurations.Results identify mechanisms involved in three components of CWL design: patient registration, patient prioritization, and patient assignment to a provider for attachment. CWL programme theory is revised to integrate mechanisms specific to primary care, where patients, rather than referring providers, are responsible for registering on the CWL, where prioritization must consider a broad range of conditions and characteristics, and where long-term acceptability of attachment is important. The study provides new insight into mechanisms that enable CWLs for unattached patients to work.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Clustering transformed compositional data using K-means,with applications in gene expression and bicycle sharing system data

Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering compositional data (i.e. data whose rows belong to the simplex) remains largely unexplored in cases where the observed value is equal or close to zero for one or more samples. This work is motivated by the analysis of two applications, both focused on the categorization of compositional profiles: (1) identifying groups of co-expressed genes from high-throughput RNA sequencing data, in which a given gene may be completely silent in one or more experimental conditions; and (2) finding patterns in the usage of stations over the course of one week in the Velib' bicycle sharing system in Paris, France. For both of these applications, we make use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension called the Log Centered Log Ratio, in conjunction with the K-means algorithm. We use a non-asymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters. Finally, we illustrate the performance of this clustering strategy, which is implemented in the Bioconductor package coseq, on both the gene expression and bicycle sharing system data.