To improve the estimation efficiency of high-dimensional regression problems, penalized regularization is routinely used. However, accurately estimating the model remains challenging, particularly in the presence of c...
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To improve the estimation efficiency of high-dimensional regression problems, penalized regularization is routinely used. However, accurately estimating the model remains challenging, particularly in the presence of correlated effects, wherein irrelevant covariates exhibit strong correlation with relevant ones. This situation, referred to as correlated data, poses additional complexities for model estimation. In this paper, we propose the elastic-net multi-step screening procedure (EnMSP), an iterative algorithm designed to recover sparse linear models in the context of correlated data. EnMSP uses a small repeated penalty strategy to identify truly relevant covariates in a few iterations. Specifically, in each iteration, EnMSP enhances the adaptive lasso method by adding a weighted l2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_2$$\end{document} penalty, which improves the selection of relevant covariates. The method is shown to select the true model and achieve the l2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_2$$\end{document}-norm error bound under certain conditions. The effectiveness of EnMSP is demonstrated through numerical comparisons and applications in financial data.
In this article, solvability of infinite system of nonlinear singular integral equations of two variables in Banach sequence spaces c(0) and l(1) is investigated. For this purpose, Hausdorff measure of noncompactness ...
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In this article, solvability of infinite system of nonlinear singular integral equations of two variables in Banach sequence spaces c(0) and l(1) is investigated. For this purpose, Hausdorff measure of noncompactness (in short, Hausdorff MNC) and Meir-Keeler condensing operators are employed. The guarantee of our results are given by some examples. Also to approach an approximation of semi-analytic solution, we introduce a coupled modified homotopy perturbation and Adomian decomposition method. Thus, an iterative algorithm is constructed to find the above solution. The numerical results show that the produced sequence to approximate the solution has a high accuracy.
This paper is concerned with the solution to the least squares problem for the quaternion Sylvester tensor equation. An iterative algorithm based on tensor format is presented to solve this problem. The convergence pr...
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This paper is concerned with the solution to the least squares problem for the quaternion Sylvester tensor equation. An iterative algorithm based on tensor format is presented to solve this problem. The convergence properties of the proposed iterative method are studied. We also consider the best approximate problem related to quaternion Sylvester tensor equation. Numerical examples are provided to confirm the theoretical results, which demonstrate that the proposed algorithm is effective and feasible for solving the least squares problem of the quaternion Sylvester tensor equation.
This is an erratum to the article entitled "Reconsidering Herbert A. Simon's Major Themes in Economics: Towards an Experimentally Grounded Capital Structure Theory Drawing from His Methodological Conjectures&...
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This is an erratum to the article entitled "Reconsidering Herbert A. Simon's Major Themes in Economics: Towards an Experimentally Grounded Capital Structure Theory Drawing from His Methodological Conjectures" published online in CE (). Specifically, Footnote no. 13 should read as follows:
Sampling and reconstruction of signals in a shift-invariant space are generally studied under the requirement that the generator is in a stronger Wiener amalgam space, and the error estimates are usually given in the ...
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Sampling and reconstruction of signals in a shift-invariant space are generally studied under the requirement that the generator is in a stronger Wiener amalgam space, and the error estimates are usually given in the sense of L-p,L-1/omega-norm. Since we often need to reflect the local characteristics of reconstructing error, the asymptotic pointwise error estimates for nonuniform and average sampling in a non-decaying shift-invariant space are discussed under the assumption that the generator is in a hybrid-norm space. Based on Lemma 2.1-Lemma 2.6, we first rewrite the iterative reconstruction algorithms for two kinds of average sampling functionals and prove their convergence. Then, the asymptotic pointwise error estimates are presented for two algorithms under the case that the average samples are corrupted by noise.
In this paper, we propose a new iterative algorithm for solving the multiple-sets split feasibility problem (MSSFP for short) and the split equality fixed point problem (SEFPP for short) with firmly quasi-nonexpansive...
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In this paper, we propose a new iterative algorithm for solving the multiple-sets split feasibility problem (MSSFP for short) and the split equality fixed point problem (SEFPP for short) with firmly quasi-nonexpansive operators or nonexpansive operators in real Hilbert spaces. Under mild conditions, we prove strong convergence theorems for the algorithm by using the projection method and the properties of projection operators. The result improves and extends the corresponding ones announced by some others in the earlier and recent literature.
In this paper, GNSS interferometric reflectometry (GNSS-IR) is firstly proposed to estimate ground surface subsidence caused by underground coal mining. Ground subsidence on the main direction of a coal seam is descri...
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In this paper, GNSS interferometric reflectometry (GNSS-IR) is firstly proposed to estimate ground surface subsidence caused by underground coal mining. Ground subsidence on the main direction of a coal seam is described by using the probability integral model (PIM) with unknown parameters. Based on the laws of reflection in geometric optics, model of GNSS signal-to-noise (SNR) observation for the tilt surface, which results from differential subsidence of ground points, is derived. Semi-cycle SNR observations fitting method is used to determine the phase of the SNR series. Phase variation of the SNR series is used to calculate reflector height of ground specular reflection point. Based on the reflector height and ground tilt angle, an iterative algorithm is proposed to determine coefficients of PIM, and thus subsidence of the ground reflection point. By using the low-cost navigational GNSS receiver and antenna, an experimental campaign was conducted to validate the proposed method. The results show that, when the maximum subsidence is 3076 mm, the maximum relative error of the proposed method-based subsidence estimation is 5.5%. This study also suggests that, based on the proposed method, the navigational GNSS instrument can be treated as a new type of sensor for continuously measuring ground subsidence deformation in a cost-effective way.
The present challenge in the ultrasonic process tomography on dispersed small particle systems is that it is hard to obtain the accurate algorithm for reconstruction. For more accurate reconstruction, this work propos...
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The present challenge in the ultrasonic process tomography on dispersed small particle systems is that it is hard to obtain the accurate algorithm for reconstruction. For more accurate reconstruction, this work proposes an improved GMRES (Generalised Minimal Residual) algorithm based on generalised minimal residual iteration and mean filtering method. To verify the feasibility of the algorithm for dispersed small particle system visualisation, a linear acoustic attenuation model is developed to obtain the projection data of ultrasonic array. Then, we compared it with the current mainstream reconstruction algorithms under the conditions of the less effective information by solving the underdetermined equations. It is shown that this method can present a high reconstruction precision in the cases of numerical simulations, and reasonably reflect the cross section of dispersed small particle distribution. In the numerical simulations, the imaging accuracy of improved GMRES algorithm can reach about 90%.
In this work, we investigate the challenging problem of channel estimation in high-mobility environ-ments for advanced mobile communication systems (5G and beyond). First, we propose an iterative algorithm for channel...
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In this work, we investigate the challenging problem of channel estimation in high-mobility environ-ments for advanced mobile communication systems (5G and beyond). First, we propose an iterative algorithm for channel estimation and symbol detection in the delay-Doppler domain for multiple-input multiple-output orthogonal time-frequency space (OTFS) system. The proposed algorithm is based on a superimposed pilot pattern to improve the spectral efficiency of the system. It iterates between data-aided channel estimation and message-passing-aided data detection. The channel estimation step is based on a threshold method. This step considers interference-plus-noise caused by the data symbols and the additive noise to adapt the threshold at each iteration. The data detection step is based on an adapted version of the message-passing algorithm proposed in the literature for uncoded OTFS. Then, to improve the channel estimation efficiency, we suggest an interference cancellation scheme executed at each iteration of the proposed algorithm. Finally, we compare the computational complexity and the achieved performance in terms of normalized mean square error of channel estimation, bit error rate, and spectral efficiency against five state-of the-art methods.(c) 2023 Elsevier B.V. All rights reserved.
When the object contains metals,its x-ray computed tomography(CT)images are normally affected by streaking *** artifacts are mainly caused by the x-ray beam hardening effects,which deviate the measurements from their ...
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When the object contains metals,its x-ray computed tomography(CT)images are normally affected by streaking *** artifacts are mainly caused by the x-ray beam hardening effects,which deviate the measurements from their true *** interesting observation of the metal artifacts is that certain regions of the metal artifacts often appear as negative pixel *** novel idea in this paper is to set up an objective function that restricts the negative pixel values in the *** must point out that the naïve idea of setting the negative pixel values in the reconstructed image to zero does not give the same *** paper proposes an iterative algorithm to optimize this objective function,and the unknowns are the metal affected *** the metal affected projections are estimated,the filtered backprojection algorithm is used to reconstruct the final *** paper applies the proposed algorithm to some airport bag CT *** bags all contain unknown metallic *** metal artifacts are effectively reduced by the proposed algorithm.
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