On the Performance of the SPICE Method
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[1] Jian Li,et al. New Method of Sparse Parameter Estimation in Separable Models and Its Use for Spectral Analysis of Irregularly Sampled Data , 2011, IEEE Transactions on Signal Processing.
[2] Jian Li,et al. SPICE: A Sparse Covariance-Based Estimation Method for Array Processing , 2011, IEEE Transactions on Signal Processing.
[3] Wenjing Liao,et al. MUSIC for Single-Snapshot Spectral Estimation: Stability and Super-resolution , 2014, ArXiv.
[4] Lihua Xie,et al. On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data , 2014, IEEE Transactions on Signal Processing.
[5] Florentina Bunea,et al. The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms , 2013, IEEE Transactions on Information Theory.
[6] C. Giraud. Introduction to High-Dimensional Statistics , 2014 .
[7] Irina Gaynanova,et al. Oracle inequalities for high-dimensional prediction , 2016, Bernoulli.
[8] Trevor Hastie,et al. Statistical Learning with Sparsity: The Lasso and Generalizations , 2015 .
[9] P. P. Vaidyanathan,et al. Pushing the Limits of Sparse Support Recovery Using Correlation Information , 2015, IEEE Transactions on Signal Processing.
[10] Björn E. Ottersten,et al. Covariance Matching Estimation Techniques for Array Signal Processing Applications , 1998, Digit. Signal Process..
[11] Håkan Hjalmarsson,et al. A Note on the SPICE Method , 2012, IEEE Transactions on Signal Processing.
[12] Emmanuel J. Candès,et al. Towards a Mathematical Theory of Super‐resolution , 2012, ArXiv.
[13] Sara A. van de Geer,et al. Sharp Oracle Inequalities for Square Root Regularization , 2015, J. Mach. Learn. Res..
[14] A. Derumigny. Improved bounds for Square-Root Lasso and Square-Root Slope , 2017, 1703.02907.
[15] S. Geer,et al. On the conditions used to prove oracle results for the Lasso , 2009, 0910.0722.
[16] Piya Pal,et al. Gridless Line Spectrum Estimation and Low-Rank Toeplitz Matrix Compression Using Structured Samplers: A Regularization-Free Approach , 2017, IEEE Transactions on Signal Processing.
[17] Gongguo Tang,et al. Approximate support recovery of atomic line spectral estimation: A tale of resolution and precision , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
[18] Emmanuel J. Candès,et al. Super-Resolution from Noisy Data , 2012, Journal of Fourier Analysis and Applications.
[19] Cishen Zhang,et al. A Discretization-Free Sparse and Parametric Approach for Linear Array Signal Processing , 2013, IEEE Transactions on Signal Processing.
[20] Heng Qiao,et al. A Universal Technique for Analysing Discrete Super-Resolution Algorithms , 2020, IEEE Signal Processing Letters.
[21] S. Geer,et al. Oracle Inequalities and Optimal Inference under Group Sparsity , 2010, 1007.1771.
[22] Yuejie Chi,et al. Off-the-Grid Line Spectrum Denoising and Estimation With Multiple Measurement Vectors , 2014, IEEE Transactions on Signal Processing.
[23] Piya Pal,et al. Guaranteed Localization of More Sources Than Sensors With Finite Snapshots in Multiple Measurement Vector Models Using Difference Co-Arrays , 2019, IEEE Transactions on Signal Processing.
[24] Petre Stoica,et al. Connection between SPICE and Square-Root LASSO for sparse parameter estimation , 2014, Signal Process..
[25] Gongguo Tang,et al. Near minimax line spectral estimation , 2013, 2013 47th Annual Conference on Information Sciences and Systems (CISS).
[26] Wenjing Liao,et al. Super-Resolution Limit of the ESPRIT Algorithm , 2019, IEEE Transactions on Information Theory.
[27] Yimin Zhang,et al. Generalized Coprime Array Configurations for Direction-of-Arrival Estimation , 2015, IEEE Transactions on Signal Processing.
[28] Heng Qiao,et al. On Overfitting in Discrete Super-Resolution Recovery , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).