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Microsystems & Nanoengineering 2023年第5期9卷 151-160页

作者： Liying Jiang Xianghao Lan Linjiao Ren Zhiyuan Jin Xuchen Shan Mingzhu Yang Lingqian Chang School of Electrical and Information Engineering Zhengzhou University of Light Industry450002 ZhengzhouChina Academy for Quantum Science and Technology Zhengzhou University of Light Industry450002 ZhengzhouChina Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical EngineeringBeihang University100191 BeijingChina School of Physics Beihang University100191 BeijingChina Beijing Research Institute of Mechanical Equipment 100143 BeijingChina

To minimize and control the transmission of infectious diseases,a sensitive,accurate,rapid,and robust assay strategy for application on-site screening is critical.Here,we report single-molecule RNA capture-assisted digital RT-LAMP(scadL)for point-of-care testing of infectious diseases.Target RNA was captured and enriched by specific capture probes and oligonucleotide probes conjugated to magnetic beads,replacing laborious RNA extraction.Droplet generation,amplification,and the recording of results are all integrated on a microfluidic chip.In assaying commercial standard samples,quantitative results precisely corresponded to the actual concentration of samples.This method provides a limit of detection of 10 copies mL−1 for the N gene within 1 h,greatly reducing the need for skilled personnel and precision instruments.The ultrasensitivity,specificity,portability,rapidity and user-friendliness make scadL a competitive candidate for the on-site screening of infectious diseases.

关键词： scad recording competitive

Local Linear Regression for Data with AR Errors

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Acta Mathematicae Applicatae Sinica 2009年第3期25卷 427-444页

作者： Runze Li Yan Li Department of Statistics and The Methodology Center Pennsylvania State University University Park PA 16802-2111 USA Cards Acquisitions Capital One Financial Inc 1680 Capital One Dr 19050-0701 McLean VA 22102 USA

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the scad penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

关键词： Auto-regressive error local linear regression partially linear model profile least squares scad

Variable selection for single-index varying-coefficient model

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Frontiers of Mathematics in China 2013年第3期8卷 541-565页

作者： Sanying FENG Liugen XUE College of Applied Sciences Beijing University of Technology Beijing 100124 China

We consider the problem of variable selection for single-index varying-coefficient model, and present a regularized variable selection procedure by combining basis function approximations with scad penalty. The proposed procedure simultaneously selects significant covariates with functional coefficients and local significant variables with parametric coefficients. With appropriate selection of the tuning parameters, the consistency of the variable selection procedure and the oracle property of the estimators are established. The proposed method can naturally be applied to deal with pure single-index model and varying-coefficient model. Finite sample performances of the proposed method are illustrated by a simulation study and the real data analysis.

关键词： Single-index varying-coefficient model variable selection scad oracle property

Partial Penalized Empirical Likelihood Ratio Test Under Sparse Case

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Acta Mathematicae Applicatae Sinica 2017年第2期33卷 327-344页

作者： Shan-shan WANG Heng-jian CUI School of Economics and Management Beihang University School of Mathematical Sciences & BCMIIS Capital Normal University

A consistent test via the partial penalized empirical likelihood approach for the parametric hy- pothesis testing under the sparse case, called the partial penalized empirical likelihood ratio （PPELR） test, is proposed in this paper. Our results are demonstrated for the mean vector in multivariate analysis and regression coefficients in linear models, respectively. And we establish its asymptotic distributions under the null hypoth- esis and the local alternatives of order n-1/2 under regularity conditions. Meanwhile, the oracle property of the partial penalized empirical likelihood estimator also holds. The proposed PPELR test statistic performs as well as the ordinary empirical likelihood ratio test statistic and outperforms the full penalized empirical like- lihood ratio test statistic in term of size and power when the null parameter is zero. Moreover, the proposed method obtains the variable selection as well as the p-values of testing. Numerical simulations and an analysis of Prostate Cancer data confirm our theoretical findings and demonstrate the promising performance of the proposed method in hypothesis testing and variable selection.

关键词： Chi-squared distribution empirical likelihood partial penalized empirical likelihood scad sparse

Variable Selection in the Partially Linear Errors-in-Variables Models for Longitudinal Data

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Acta Mathematicae Applicatae Sinica 2012年第4期28卷 769-780页

作者： Yi-ping YANG Liu-gen XUE Wei-hu CHENG College of Mathematics and Statistics Chongqing Technology and Business University Department of Applied Mathematics Beijing University of Technology

This paper proposes a new approach for variable selection in partially linear errors-in-variables （EV） models for longitudinal data by penalizing appropriate estimating functions. We apply the scad penalty to simultaneously select significant variables and estimate unknown parameters. The rate of convergence and the asymptotic normality of the resulting estimators are established. Furthermore, with proper choice of regularization parameters, we show that the proposed estimators perform as well as the oracle procedure. A new algorithm is proposed for solving penalized estimating equation. The asymptotic results are augmented by a simulation study.

关键词： errors-in-variables variable selection estimating function oracle scad

Variable Selection for Semiparametric Varying-Coefficient Partially Linear Models with Missing Response at Random

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Acta Mathematica Sinica,English Series 2011年第11期27卷 2205-2216页

作者： Pei Xin ZHAO Liu Gen XUE Department of Mathematics Hechi University Yizhou 546300 P. R. China College of Applied Sciences Beijing University of Technology Beijing 100124 P. R. China

In this paper, we present a variable selection procedure by combining basis function approximations with penalized estimating equations for semiparametric varying-coefficient partially linear models with missing response at random. The proposed procedure simultaneously selects significant variables in parametric components and nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency of the variable selection procedure and the convergence rate of the regularized estimators. A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.

关键词： Semiparametric varying-coefficient partially linear model variable selection scad missing data

Variable Selection for Generalized Varying Coefficient Partially Linear Models with Diverging Number of Parameters

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Acta Mathematicae Applicatae Sinica 2012年第2期28卷 237-246页

作者： Zheng-yan Lin Yu-ze Yuan Department of Mathematics Zhejiang University Hangzhou 310027 China

Semiparametric models with diverging number of predictors arise in many contemporary scientific areas. Variable selection for these models consists of two components： model selection for non-parametric components and selection of significant variables for the parametric portion. In this paper, we consider a variable selection procedure by combining basis function approximation with scad penalty. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of tuning parameters, we establish the consistency and sparseness of this procedure.

关键词： generalized linear model varying coefficient high dimensionality scad basis function.

基于大规模高维线性回归模型的分布式计算方法研究

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应用数学学报 2022年第3期45卷 339-354页

作者：潘莹丽刘展闫玲玲湖北大学数学与统计学学院应用数学湖北省重点实验室武汉430062

大数据背景下挖掘大规模高维数据所隐藏的信息备受关注.本文主要目的是采用分布式优化方法解决加scad和Adaptive LASSO惩罚的高维线性回归中的参数估计和变量选择问题.主要方法是通过构造全局损失函数的一个交互有效的正则化替代损失函... 详细信息

大数据背景下挖掘大规模高维数据所隐藏的信息备受关注.本文主要目的是采用分布式优化方法解决加scad和Adaptive LASSO惩罚的高维线性回归中的参数估计和变量选择问题.主要方法是通过构造全局损失函数的一个交互有效的正则化替代损失函数,把基于全局损失函数的优化问题转化为基于替代损失函数的优化问题.本文设计的修正的ADMM算法,在计算上,只需要子机器基于局部数据计算梯度,而主机器进行参数估计和变量选择.在主从机器交互复杂度上,基于替代损失函数所得的估计误差收敛于基于全局损失函数所得的估计误差.通过模拟和实证研究进一步验证本文提出的分布式计算方法在实际生活中的可行性和实用性.

关键词：分布式优化 scad Adaptive LASSO 高维线性回归修正的ADMM算法

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基于数值模拟的新建片区海绵设计特点分析

给水排水 2022年第S1期48卷 1053-1056,1061页

作者：张楠成志轩上海市政工程设计研究总院(集团)有限公司上海200092 丹华水利环境技术(上海)有限公司上海200235

以国内南方新建设片区为例,结合地块的海绵设计要求,构建由土壤渗透能力、地下水位、城市下垫面和地形因子4个指标的综合评价体系,从日降雨和多年降雨这两个尺度进行径流控制率评估,对海绵城市设计的特点进行了分析,针对不同土地利用类... 详细信息

以国内南方新建设片区为例,结合地块的海绵设计要求,构建由土壤渗透能力、地下水位、城市下垫面和地形因子4个指标的综合评价体系,从日降雨和多年降雨这两个尺度进行径流控制率评估,对海绵城市设计的特点进行了分析,针对不同土地利用类型、不同降雨边界,分析了LID设施对径流(径流量与径流污染)的影响。

关键词：海绵城市数值模拟 scad 径流控制

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基于scad变量选择的多元控制图研究

基于SCAD变量选择的多元控制图研究

作者：陈偲尔厦门大学

学位级别：硕士

控制图技术是统计过程控制最主要的工具,如今已被广泛应用于化工、电子、冶金、食品和医疗服务等行业。随着科技的发展,人们可获取的过程属性或产品特征与日俱增,为质量管理带来更全面的信息的同时,也使得噪音累积、偶然内生性等问题频... 详细信息

控制图技术是统计过程控制最主要的工具,如今已被广泛应用于化工、电子、冶金、食品和医疗服务等行业。随着科技的发展,人们可获取的过程属性或产品特征与日俱增,为质量管理带来更全面的信息的同时,也使得噪音累积、偶然内生性等问题频发,大大影响了参数估计的准确性及统计过程控制的稳定性。将含稀疏性惩罚项的变量选择方法应用于传统多元统计过程控制(Multivariate Statistical Process Control,MSPC)的控制图中,是应对“维度祸端”所制造麻烦的良好方案。本文首先基于广义似然比检验构建了基于scad变量选择的多元控制图(scad-MSPC控制图),推导计算出该控制图的统计量并阐述了该技术的具体实施步骤,进一步通过数值模拟确定以AIC准则作为惩罚似然函数调整参数的选择器。接着,本文以固定受控平均运行链长下的失控平均运行链长为指标,对比了scad-MSPC控制图与同构建框架下的Hotelling T2控制图、VS-MSPC控制图以及LASSO-MSPC控制图的监控效果。模拟结果表明,在不同情况下,变量选择方法对多元控制图具有一定程度的改善作用,而scad-MSPC控制图的整体表现优于-VS-MSPC控制图和LASSO-MSPC控制图,不依赖于预设参数且失控平均运行链长更小,检出率更高,更为稳健。另外,在根本原因诊断上,scad-MSPC控制图的性能也优于VS-MSPC控制图和LASSO-MSPC控制图。最后,本文还运用scad-MSPC控制图对铝冶炼厂的数据进行分析研究,验证了所构建控制图的可操作性与现实意义。

关键词：多元控制图 scad 广义似然比检验平均运行链长