This article develops a procedure for screening variables, in ultra high-di- mensional settings, based on their predictive significance. This is achieved by ranking the variables according to the variance of their res...This article develops a procedure for screening variables, in ultra high-di- mensional settings, based on their predictive significance. This is achieved by ranking the variables according to the variance of their respective marginal regression functions (RV-SIS). We show that, under some mild technical conditions, the RV-SIS possesses a sure screening property, which is defined by Fan and Lv (2008). Numerical comparisons suggest that RV-SIS has competitive performance compared to other screening procedures, and outperforms them in many different model settings.展开更多
稀疏表示图像去噪方法中噪声方差,需事先假定,且K-SVD(k-means singular value decomposition)字典学习方法难以解决参数自动选择问题。为此,由于图像在梯度域的稀疏性优于空间域,提出一种梯度域非参数贝叶斯字典学习图像去噪方法。考...稀疏表示图像去噪方法中噪声方差,需事先假定,且K-SVD(k-means singular value decomposition)字典学习方法难以解决参数自动选择问题。为此,由于图像在梯度域的稀疏性优于空间域,提出一种梯度域非参数贝叶斯字典学习图像去噪方法。考虑非参数贝叶斯字典学习图像去噪模型是一个多变量耦合问题,难以求解,利用Bregman和交替迭代方法把该问题分解为多个子问题,利用最小二乘方法和BPFA(beta process factor analysis)字典学习方法分别求解这些子问题,滤除图像噪声,保留原图像的有用信息。实验结果表明,提出方法较GradDLRec算法的峰值信噪比平均可提高1.4 dB左右,重建的图像细节信息更丰富,且该方法具有良好收敛性。展开更多
This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine ...This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building.展开更多
统计过程控制(statistical process contor,SPC)是应用统计方法对过程中的各个阶段进行监控,从而达到改进和保证质量的目的.本文在一些重要的前沿问题上展开研究,其中包括profile数据过程的监控和诊断、监测drift飘移的控制图、多元过...统计过程控制(statistical process contor,SPC)是应用统计方法对过程中的各个阶段进行监控,从而达到改进和保证质量的目的.本文在一些重要的前沿问题上展开研究,其中包括profile数据过程的监控和诊断、监测drift飘移的控制图、多元过程控制和多阶段过程的检测和诊断.本文引入并开发各种新的统计技术,紧密结合计算算法,解决这些当前质量控制领域研究的难点问题.展开更多
In this paper, we consider the issue of variable selection in partial linear single-index models under the assumption that the vector of regression coefficients is sparse. We apply penalized spline to estimate the non...In this paper, we consider the issue of variable selection in partial linear single-index models under the assumption that the vector of regression coefficients is sparse. We apply penalized spline to estimate the nonparametric function and SCAD penalty to achieve sparse estimates of regression parameters in both the linear and single-index parts of the model. Under some mild conditions, it is shown that the penalized estimators have oracle property, in the sense that it is asymptotically normal with the same mean and covariance that they would have if zero coefficients are known in advance. Our model owns a least square representation, therefore standard least square programming algorithms can be implemented without extra programming efforts. In the meantime, parametric estimation, variable selection and nonparametric estimation can be realized in one step, which incredibly increases computational stability. The finite sample performance of the penalized estimators is evaluated through Monte Carlo studies and illustrated with a real data set.展开更多
文摘This article develops a procedure for screening variables, in ultra high-di- mensional settings, based on their predictive significance. This is achieved by ranking the variables according to the variance of their respective marginal regression functions (RV-SIS). We show that, under some mild technical conditions, the RV-SIS possesses a sure screening property, which is defined by Fan and Lv (2008). Numerical comparisons suggest that RV-SIS has competitive performance compared to other screening procedures, and outperforms them in many different model settings.
文摘稀疏表示图像去噪方法中噪声方差,需事先假定,且K-SVD(k-means singular value decomposition)字典学习方法难以解决参数自动选择问题。为此,由于图像在梯度域的稀疏性优于空间域,提出一种梯度域非参数贝叶斯字典学习图像去噪方法。考虑非参数贝叶斯字典学习图像去噪模型是一个多变量耦合问题,难以求解,利用Bregman和交替迭代方法把该问题分解为多个子问题,利用最小二乘方法和BPFA(beta process factor analysis)字典学习方法分别求解这些子问题,滤除图像噪声,保留原图像的有用信息。实验结果表明,提出方法较GradDLRec算法的峰值信噪比平均可提高1.4 dB左右,重建的图像细节信息更丰富,且该方法具有良好收敛性。
文摘This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building.
文摘统计过程控制(statistical process contor,SPC)是应用统计方法对过程中的各个阶段进行监控,从而达到改进和保证质量的目的.本文在一些重要的前沿问题上展开研究,其中包括profile数据过程的监控和诊断、监测drift飘移的控制图、多元过程控制和多阶段过程的检测和诊断.本文引入并开发各种新的统计技术,紧密结合计算算法,解决这些当前质量控制领域研究的难点问题.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.1117111211201190)+1 种基金the Doctoral Fund of Ministry of Education of China(Grant No.20130076110004)the 111 Project of China(Grant No.B14019)
基金Supported by the National Natural Science Foundation of China(No.11671096)
文摘In this paper, we consider the issue of variable selection in partial linear single-index models under the assumption that the vector of regression coefficients is sparse. We apply penalized spline to estimate the nonparametric function and SCAD penalty to achieve sparse estimates of regression parameters in both the linear and single-index parts of the model. Under some mild conditions, it is shown that the penalized estimators have oracle property, in the sense that it is asymptotically normal with the same mean and covariance that they would have if zero coefficients are known in advance. Our model owns a least square representation, therefore standard least square programming algorithms can be implemented without extra programming efforts. In the meantime, parametric estimation, variable selection and nonparametric estimation can be realized in one step, which incredibly increases computational stability. The finite sample performance of the penalized estimators is evaluated through Monte Carlo studies and illustrated with a real data set.
