Sparse partial least squares regression for simultaneous dimension reduction and variable selection

Summary.  Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data.     

空间数据分析的发展

空间统计学是研究空间问题的一门学科,它是应用数学快速发展的一个分支。尽管传统的数据分析中有许多很好的方法,但却不能完全地套用于空间数据的分析。空间模型的估计不仅与各种回归形式的假设有关,而且还与空间相关、空间异质的特性有关。从空间模型及推断、适应性估计、非参数回归、空间不相关性检验几个方面研究了空间数据分析方法的发展以及未来的趋势。     

Estimation of covariance matrix via the sparse Cholesky factor with lasso

In this paper, we discuss a parsimonious approach to estimation of high-dimensional covariance matrices via the modified Cholesky decomposition with lasso. Two different methods are proposed. They are the equi-angular and equi-sparse methods. We use simulation to compare the performance of the proposed methods with others available in the literature, including the sample covariance matrix, the banding method, and the L₁-penalized normal loglikelihood method. We then apply the proposed methods to a portfolio selection problem using 80 series of daily stock returns. To facilitate the use of lasso in high-dimensional time series analysis, we develop the dynamic weighted lasso (DWL) algorithm that extends the LARS-lasso algorithm. In particular, the proposed algorithm can efficiently update the lasso solution as new data become available. It can also add or remove explanatory variables. The entire solution path of the L₁-penalized normal loglikelihood method is also constructed.     

Nonlinear regression modeling via the lasso-type regularization

We consider the problem of constructing nonlinear regression models with Gaussian basis functions, using lasso regularization. Regularization with a lasso penalty is an advantageous in that it estimates some coefficients in linear regression models to be exactly zero. We propose imposing a weighted lasso penalty on a nonlinear regression model and thereby selecting the number of basis functions effectively. In order to select tuning parameters in the regularization method, we use a deviance information criterion proposed by Spiegelhalter et al. (2002), calculating the effective number of parameters by Gibbs sampling. Simulation results demonstrate that our methodology performs well in various situations.     

Regularization and variable selection via the elastic net

Summary.  We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The elastic net is particularly useful when the number of predictors ( p ) is much bigger than the number of observations ( n ). By contrast, the lasso is not a very satisfactory variable selection method in the p ≫ n case. An algorithm called LARS-EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lasso.     

EMDR、适应性信息加工与个案概念化(下)

EMDR是一个整合性的、以咨客为中心的心理治疗方法。该方法用于治疗那些不是由器质性损伤为病理学基础的心理问题,它的治疗重点是处理大脑的信息加工系统中的痛苦记忆。EMDR既能处理那些引起困扰的经历和体验,也能帮助咨客获得达到更健康的心理状态所需要的经历和体验。综览EMDR的历史、发展和研究,它是一个主要以经验为支持的治疗方法。本文在阐述适应性信息加工模型之后,用一个延续的个案实例,来说明EMDR的个案概念化和8个阶段的治疗。EMDR技术主要是加工那些有病理学基础的早期记忆和引发不良功能的当前情境,并为未来适应性的行为提供模板,包括克服技能不足和(或)发展性的不足所需的知识与行为。整合的EMDR与家庭系统观点的好处是可以提供最综合的治疗效果。     

基于EMD的非线性信号自适应分析

根据EMD分解原理,提出通过选择平均包络中的突变点确定自适应滤波器截止分量阶,以此构造时空滤波器组进行非线性信号自适应分析的方法,成功应用于分析波音737飞机涡轮轴振动信号。从噪声中自适应地提取随转速变化的信号分量和低频振动分量,与小波分析方法相比,在轴向位移检测中取得了更为满意的效果。该方法不仅改进了涡轮轴振动信号分析方法,也适用于解决其他非线性信号的分析问题。     

一种适用于远程医疗的水印算法

研究了脑电图(EEG)的水印算法,提出了一种基于自适应滤波的数字水印算法。实验仿真结果表明水印是不可见的,经过低通滤波、中值滤波、维纳滤波、JPEG压缩、小角度旋转等图像处理操作后仍可检验出水印,比空域算法有更高的鲁棒性。     

宽频带ADM系统分析与实验研究

实现了处理宽频带信号的自适应增量调制(ADM)及解调系统,并对该系统进行了一系列研究,实验结果与理论分析基本一致,系统在10Hz～10kHz工作带宽范围内具有良好的响应能力,其采样率、步长数和记忆比特均可变,适合不同条件下的应用要求。本研究在音乐信号处理实验上获得了满意的结果,对于语音、地震等信号的处理亦具有广泛的应用前景。     

城市废弃物资源化共生网络脆弱性影响机制——基于SCP与CAS融合的分析视角

在内外因素的交互干扰下,城市废弃物资源化共生网络表现得格外脆弱,探寻其脆弱性的理论内涵、影响因素及其作用机制是开展脆弱性治理的前提。借鉴复杂系统脆弱性理论,从能力和绩效两个角度解析其理论内涵,指出系统脆弱性在能力上可以用敏感性、适应性和恢复力予以刻画,在绩效上表现为增值性不强、共享性不足和生态性不佳。通过梳理国内外研究文献,从共生单元、网络结构、共生机制、网络氛围和环境不确定性5个维度提炼出脆弱性影响因素;以网络动态能力为结合点,将SCP（Structure Conduct Performance）和CAS（Complexity Adaptive System）两种理论有机融合,构建一个以共生单元素质、网络结构特征、共生机制属性、政策有效性为外因变量,以网络动态能力为中介变量,以网络脆弱性为结果变量,以网络氛围和环境不确定性为调节变量的理论框架。未来可利用大样本调查方法对该理论框架进行实证检验,探讨城市废弃物资源化共生网络脆弱性的形成机理。