Sparse additive models

Summary.  We present a new class of methods for high dimensional non-parametric regression and classification called sparse additive models. Our methods combine ideas from sparse linear modelling and additive non-parametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. Sparse additive models are essentially a functional version of the grouped lasso of Yuan and Lin. They are also closely related to the COSSO model of Lin and Zhang but decouple smoothing and sparsity, enabling the use of arbitrary non-parametric smoothers. We give an analysis of the theoretical properties of sparse additive models and present empirical results on synthetic and real data, showing that they can be effective in fitting sparse non-parametric models in high dimensional data.     

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.     

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.     

论适应性表征及其方法论意义

适应性是主体适应其环境的特性,是物理演化系统和生物演化系统的本质属性,反映在认知上就是适应性表征。适应性表征也因此成为知识显现的方式和创造的核心,具有协调性、匹配性、互补性、模拟性和类比性,其表现方式有直接具象表征、间接具象表征、直接抽象表征和间接抽象表征,分别对应于经验主义、建构经验主义、理性主义和科学实在论。适应性表征的实现是通过科学理论核心概念的变化、定律的凝练、理论的更替、模型推理和世界观的改变展开的,具有经验上的适当性。这种经验适当性不仅是感知层次的体验,更是一种认知模式。在认知意义上,经验也是基于心理模型的,心理建模的路径有思想语言、心理表象、心理命题、思想实验和心理模拟推理。这种基于心理建模的适应性表征为科学认知和科学发现提供了有益的方法论。     

变革社会中的政治资源配置

政治资源配置是变革社会中的一个重要发展参数。社会转型时期 ,既有政治资源的配置结构有所松动 ,出现分散性发展的倾向 ,而其既有之高度集中性也愈益显示某些弊端。因应形势的变化 ,中国共产党有必要引导政治资源配置作出适应性调整 ,促使其向合理性和适度社会倾向性变革 ,并增强自身开发、掌用政治资源的能力 ,确保党在政治资源配置中的主导地位     

HF通信网中的突发自适应选频技术

介绍了突发自适应选频技术在ＨＦ通信网中的应用，分析了突发自适应选频的特点及网络应用的优势，设计了一套用于网络突发选频的协议，讨论了协议中的重要参数设定和协议性能，并提出了多种方案供优化     

Random variate transformations in the Gibbs sampler: issues of efficiency and convergence

In the non-conjugate Gibbs sampler, the required sampling from the full conditional densities needs the adoption of black-box sampling methods. Recent suggestions include rejection sampling, adaptive rejection sampling, generalized ratio of uniforms, and the Griddy-Gibbs sampler. This paper describes a general idea based on variate transformations which can be tailored in all the above methods and increase the Gibbs sampler efficiency. Moreover, a simple technique to assess convergence is suggested and illustrative examples are presented.     

企业战略与环境的适应性:一个复杂适应系统的视角

企业战略的重要特性之一是它对环境的适应性,它强调企业组织能够运用自己的资源去适应组织外部环境和内在条件的变化。企业环境是不确定的,面对不同层次的不确定性环境,企业的适应性战略主要包括外包战略、联盟战略、柔性战略。     

对高校新生适应期目标缺损现象的研究

高校新生进入大学后在学习、生活、心理等方面有一个适应期，适应期的长短直接影响着大学生的成长、成才。在适应期中。大学新生普遍存在目标缺损现象，即大学新生在一定环境条件下，按一定标准所确立的目标（或潜在目标）与高校的实际情况存在偏差。文章从大学新生目标缺损现象的表现形式入手，从思想政治素质、大学的学习和生活方式、大学的教学和管理模式、大学新生的特点等方面分析研宛造成目标缺损现象的主要原因，提出了大学生目标缺损的校正对策。     

从翻译适应选择论看译者的选择性适应与适应性选择——以鲁迅早期译介域外科学小说的个案研究为例

翻译适应选择论将达尔文生物进化论中的＂自然选择＂、＂适者生存＂学说引入翻译理论研究,凸显了译者的主体性——译者的选择性适应与适应性选择,为翻译研究开辟了一条新路径。在翻译适应选择论的指导下,以鲁迅早期译介域外科学小说为个案研究对象,指出译者在翻译过程中要充分发挥主观能动性,选择、适应特定的翻译生态环境,并依此择译合适的文本、实现＂多维度适应与适应性选择转换＂,以提高＂最佳翻译＂的＂整合适应选择度＂。