Interquantile shrinkage in additive models

In this paper, we investigate the commonality of nonparametric component functions among different quantile levels in additive regression models. We propose two fused adaptive group Least Absolute Shrinkage and Selection Operator penalties to shrink the difference of functions between neighbouring quantile levels. The proposed methodology is able to simultaneously estimate the nonparametric functions and identify the quantile regions where functions are unvarying, and thus is expected to perform better than standard additive quantile regression when there exists a region of quantile levels on which the functions are unvarying. Under some regularity conditions, the proposed penalised estimators can theoretically achieve the optimal rate of convergence and identify the true varying/unvarying regions consistently. Simulation studies and a real data application show that the proposed methods yield good numerical results.     

The social security number: A small device underpinning big systems

The possession and use of a personal social security number helps to structure people's daily lives. However, despite its fundamental normative importance, the social security number remains a little‐known entity. Increasingly universal and yet diverse in form, it is a legal and technical norm which is as much a mechanism for surveillance and monitoring as it is a necessary instrument for giving effect to social rights. Analysis of this constituent element of social security systems permits as assessment of some of the technical difficulties presented by the ever‐increasing movement of people and data. Overcoming these technical difficulties should permit to envisage a first technical step towards realizing a universal and global social security system.     

Approximating the risk score for disease diagnosis using MARS

In disease screening and diagnosis, often multiple markers are measured and combined to improve the accuracy of diagnosis. McIntosh and Pepe [Combining several screening tests: optimality of the risk score, Biometrics 58 (2002), pp. 657–664] showed that the risk score, defined as the probability of disease conditional on multiple markers, is the optimal function for classification based on the Neyman–Pearson lemma. They proposed a two-step procedure to approximate the risk score. However, the resulting receiver operating characteristic (ROC) curve is only defined in a subrange (L, h) of false-positive rates in (0,1) and the determination of the lower limit L needs extra prior information. In practice, most diagnostic tests are not perfect, and it is usually rare that a single marker is uniformly better than the other tests. Using simulation, I show that multivariate adaptive regression spline is a useful tool to approximate the risk score when combining multiple markers, especially when ROC curves from multiple tests cross. The resulting ROC is defined in the whole range of (0,1) and is easy to implement and has intuitive interpretation. The sample code of the application is shown in the appendix.     

Doubly adaptive biased coin designs with heterogeneous responses

Doubly adaptive biased coin design (DBCD) is an important family of response-adaptive randomization procedures for clinical trials. It uses sequentially updated estimation to skew the allocation probability to favor the treatment that has performed better thus far. An important assumption for the DBCD is the homogeneity assumption for the patient responses. However, this assumption may be violated in many sequential experiments. Here we prove the robustness of the DBCD against certain time trends in patient responses. Strong consistency and asymptotic normality of the design are obtained under some widely satisfied conditions. Also, we propose a general weighted likelihood method to reduce the bias caused by the heterogeneity in the inference after a trial. Some numerical studies are also presented to illustrate the finite sample properties of DBCD.     

A simple two-stage design for quantitative responses with application to a study in diabetic neuropathic pain

Two-stage designs offer substantial advantages for early phase II studies. The interim analysis following the first stage allows the study to be stopped for futility, or more positively, it might lead to early progression to the trials needed for late phase II and phase III. If the study is to continue to its second stage, then there is an opportunity for a revision of the total sample size. Two-stage designs have been implemented widely in oncology studies in which there is a single treatment arm and patient responses are binary. In this paper the case of two-arm comparative studies in which responses are quantitative is considered. This setting is common in therapeutic areas other than oncology. It will be assumed that observations are normally distributed, but that there is some doubt concerning their standard deviation, motivating the need for sample size review. The work reported has been motivated by a study in diabetic neuropathic pain, and the development of the design for that trial is described in detail.     

Peer paradox: the tensions that peer relationships raise for vulnerable youth

The contribution peer relationships make to positive adolescent development is well recognized. Accordingly, peer problem measures typically assess youth with few age‐appropriate peers as having peer problems. Yet, youth facing high levels of personal and/or social adversity may reduce their association with antisocial peers as part of coping or risk mitigation strategies. While such strategies will result in higher scores on peer problem measures, they may also facilitate resilience and constitute a resource social workers can draw on in their work with youth. To test this proposition of peer adaptation as a risk mitigation strategy, mixed‐methods data relating to two groups of youth who were exposed to different levels of adversity were compared on a standardized peer problem measure and a range of risk measures. Qualitative interviews extended this data and explored vulnerable youth perceptions of social withdrawal as a coping strategy. Results from the survey and qualitative data indicated that a subgroup of youth facing high levels of adversity restricted association with antisocial peers to reduce their behavioural risks. However, without adequate support from adults in both formal support systems and youths' social ecologies to compensate for the loss of peer friendships, this strategy did not reduce behavioural risk in the medium term. The social withdrawal strategy also appeared to heighten mental health concerns for these youth. The implications of this finding for the development of policy and practice with vulnerable youth are discussed.     

坚持劳动价值论与价值转形研究

劳动价值论是整个马克思主义经济学体系的基础,循着抽象上升到具体的研究思路,价值的简单形态必然要向经济现实逼近,即价值要向生产价格的转化,而转换过程中两个总计方程是否相等,则一直是马克思主义经济学中最具争议的问题,这绝不仅仅是一个技术问题,而是关系到马克思主义经济学理论体系的根本所在。它既解决了价值规律和生产价格规律之间的矛盾,也说明了产品价值以生产价格形式在全社会形成的分配关系。因此,研究转形问题具有重要的理论和现实意义。本文将马克思对转形问题的阐述细化为若干利润平均化周期,并通过对平均利润率的分析,指出由于部分剩余价值没有参加利润平均化过程,所以出现了剩余价值总额不等于利润总额的现象,但是这并不能改变价值向生产价格的转化的真实存在,也不与劳动价值论矛盾。     

内隐记忆研究新进展

文章简要介绍了内隐记忆在研究方法、特点机制、神经心理学基础及应用研究等方面近年来国内外的一些有代表性的进展情况 ,指出内隐记忆的研究和应用意义重大 ,前景看好     

预期后悔、归因风格对购买决策中信息加工的影响

在以往研究的基础上,引入预期后悔情绪（高、低）和归因风格（积极归因风格、消极归因风格）变量,以购买汽车为决策任务,采用决策研究的信息板技术呈现实验任务,探讨了预期后悔情绪与归因风格对购买决策信息加工的影响。结果显示：（1）在信息加工时间和信息搜索深度上,预期后悔情绪与归因风格主效应以及交互作用显著,积极归因风格比消极归因风格的被试更容易受到预期后悔情绪的影响,表现为更长时间的信息加工和更深的信息搜索;（2）在信息搜索模式上,预期后悔情绪与归因风格的交互作用不显著,高预期后悔情绪的被试多倾向于基于属性的信息搜索模式。