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Ben Brown Christopher J. Miller Julian Wolfson 《Journal of computational and graphical statistics》2017,26(3):579-588
Most variable selection techniques for high-dimensional models are designed to be used in settings, where observations are independent and completely observed. At the same time, there is a rich literature on approaches to estimation of low-dimensional parameters in the presence of correlation, missingness, measurement error, selection bias, and other characteristics of real data. In this article, we present ThrEEBoost (Thresholded EEBoost), a general-purpose variable selection technique which can accommodate such problem characteristics by replacing the gradient of the loss by an estimating function. ThrEEBoost generalizes the previously proposed EEBoost algorithm (Wolfson 2011) by allowing the number of regression coefficients updated at each step to be controlled by a thresholding parameter. Different thresholding parameter values yield different variable selection paths, greatly diversifying the set of models that can be explored; the optimal degree of thresholding can be chosen by cross-validation. ThrEEBoost was evaluated using simulation studies to assess the effects of different threshold values on prediction error, sensitivity, specificity, and the number of iterations to identify minimum prediction error under both sparse and nonsparse true models with correlated continuous outcomes. We show that when the true model is sparse, ThrEEBoost achieves similar prediction error to EEBoost while requiring fewer iterations to locate the set of coefficients yielding the minimum error. When the true model is less sparse, ThrEEBoost has lower prediction error than EEBoost and also finds the point yielding the minimum error more quickly. The technique is illustrated by applying it to the problem of identifying predictors of weight change in a longitudinal nutrition study. Supplementary materials are available online. 相似文献
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Cartoon-like images, i.e., C2 functions which are smooth apart from a C2 discontinuity curve, have by now become a standard model for measuring sparse (nonlinear) approximation properties of directional representation systems. It was already shown that curvelets, contourlets, as well as shearlets do exhibit sparse approximations within this model, which are optimal up to a log-factor. However, all those results are only applicable to band-limited generators, whereas, in particular, spatially compactly supported generators are of uttermost importance for applications.In this paper, we present the first complete proof of optimally sparse approximations of cartoon-like images by using a particular class of directional representation systems, which indeed consists of compactly supported elements. This class will be chosen as a subset of (non-tight) shearlet frames with shearlet generators having compact support and satisfying some weak directional vanishing moment conditions. 相似文献
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This paper deals with the problem of multivariate copula density estimation. Using wavelet methods we provide two shrinkage procedures based on thresholding rules for which knowledge of the regularity of the copula density to be estimated is not necessary. These methods, said to be adaptive, have proved to be very effective when adopting the minimax and the maxiset approaches. Moreover we show that these procedures can be discriminated in the maxiset sense. We provide an estimation algorithm and evaluate its properties using simulation. Finally, we propose a real life application for financial data. 相似文献
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基于小波包变换和数学形态学结合的光谱去噪方法研究 总被引:8,自引:0,他引:8
对反射光谱数据进行去噪是提高光谱信息准确度的前提。传统时域平滑和频域去噪方法存在诸多缺点,本文首次将广义形态滤波方法用于可见近红外光谱的去噪处理,并提出基于小波包变换和数学形态学结合的光谱去噪方法。使用USGS光谱库中的植被光谱进行实验,采用信噪比(SNR)、均方误差根(RMSE)、波形相似度(NCC)和平滑度(SR)四个指标来评估去噪效果。结果表明,小波包最佳基阈值法和广义形态滤波法都能较好地保持波形和平滑度,广义形态滤波法能较好地消除幅值较大的随机噪声,但其对连续随机噪声中幅值较小的噪声成分不能有效消除; 而小波包最佳基阈值法不能有效消除幅值较大的噪声成分; 二者结合的方法组合了这两者的优点,使得幅值较大、较小的噪声成分都能较好地消除,同时还提高了相似度和平滑度指标,充分表明小波包最佳基阈值与广义形态滤波结合的方法是一种更好的可见光近红外光谱去噪方法。 相似文献
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《Applied and Computational Harmonic Analysis》2020,48(1):482-495
This letter establishes sufficient conditions for the sparse multiple measurement vector (MMV) or row-sparse matrix approximation problem for the Rank Aware Row Thresholding (RART) algorithm. Using the rank aware selection operator to define RART results in discrete MUltiple SIgnal Classification (MUSIC) from array signal processing. When the sensing matrix is drawn from the random Gaussian matrix ensemble, we establish that the rank of the row-sparse measurement matrix in the noiseless row-sparse recovery problem allows RART (MUSIC) to reduce the effect of the penalty term that is present in traditional compressed sensing results and simultaneously provides a row-rank deficient recovery result for MUSIC. Empirical evidence shows that Thresholding closely matches RART in successful row-sparse approximation. The theoretical and empirical evidence provides further support for the conjecture that the thresholding operator in more sophisticated greedy algorithms is the source of their observed rank awareness. 相似文献
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利用模拟退火遗传算法实现图像阈值分割 总被引:1,自引:0,他引:1
本文提出了一种利用模拟退火算法和遗传算法相结合的图像阈值分割算法,试验结果表明该算法增强了算法的全局收敛性,加快了算法的收敛速度,提高了图像阈值分割的效率. 相似文献
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Cross-validation has been successfully used in various areas of statistics. However, it has not been used much in wavelet
shrinkage estimation because fast wavelet methods cannot be applied to deleted data. In this paper, we show this problem can
be avoided by using a fast imputation of data. This allows level-dependent cross- validation which is attractive to data with
different sparseness. The proposed methods can be easily extended to higher dimensional problem such as image. Results from
simulation and examples demonstrate the promising empirical properties of the procedure. In particular, the methods proposed
in this work provide outstanding results for non-Gaussian noises because cross-validation is not based on normality assumptions. 相似文献