Sparse Estimation of Spike-Triggered Average

Shimpei Yotsukura; Toshiaki Omori; Kenji Nagata; Masato Okada

doi:10.2197/ipsjtrans.7.52

Sparse Estimation of High-Dimensional Inverse Covariance Matrices with Explicit Eigenvalue Constraints

Journal of the Operations Research Society of China ◽

10.1007/s40305-021-00351-y ◽

2021 ◽

Author(s):

Yun-Hai Xiao ◽

Pei-Li Li ◽

Sha Lu

Keyword(s):

Covariance Matrices ◽

High Dimensional ◽

Sparse Estimation

Download Full-text

Ensemble sparse estimation of covariance structure for exploring genetic disease data

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2021.107220 ◽

2021 ◽

pp. 107220

Author(s):

Xiaoning Kang ◽

Mingqiu Wang

Keyword(s):

Genetic Disease ◽

Covariance Structure ◽

Sparse Estimation

Download Full-text

A Novel Detection Scheme with Multiple Observations for Sparse Signal Based on Likelihood Ratio Test with Sparse Estimation

Mathematical Problems in Engineering ◽

10.1155/2016/8535486 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14

Author(s):

Hongbo Zhao ◽

Lei Chen ◽

Wenquan Feng ◽

Chuan Lei

Keyword(s):

Likelihood Ratio ◽

Likelihood Ratio Test ◽

Prior Information ◽

Finite Size ◽

Sparse Signals ◽

Ratio Test ◽

Sparse Estimation ◽

Detection Scheme ◽

Error Exponent ◽

Multiple Observations

Recently, the problem of detecting unknown and arbitrary sparse signals has attracted much attention from researchers in various fields. However, there remains a peck of difficulties and challenges as the key information is only contained in a small fraction of the signal and due to the absence of prior information. In this paper, we consider a more general and practical scenario of multiple observations with no prior information except for the sparsity of the signal. A new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is presented. Under the Neyman-Pearson testing framework, LRT-SE estimates the unknown signal by employing thel1-minimization technique from compressive sensing theory. The detection performance of LRT-SE is preliminarily analyzed in terms of error probabilities in finite size and Chernoff consistency in high dimensional condition. The error exponent is introduced to describe the decay rate of the error probability as observations number grows. Finally, these properties of LRT-SE are demonstrated based on the experimental results of synthetic sparse signals and sparse signals from real satellite telemetry data. It could be concluded that the proposed detection scheme performs very close to the optimal detector.

Download Full-text

Continuous Exact ℓ0 based Sparse Estimation for GNSS Multipath Mitigation in an Urban Environment

2021 International Conference on Communication, Control and Information Sciences (ICCISc) ◽

10.1109/iccisc52257.2021.9485009 ◽

2021 ◽

Author(s):

Uppuluri S V N Sai Teja ◽

Madhu Ramarakula

Keyword(s):

Urban Environment ◽

Multipath Mitigation ◽

Sparse Estimation

Download Full-text

Sparse estimation of backscattered echoes from underwater object using integrated dictionaries

The Journal of the Acoustical Society of America ◽

10.1121/1.5083830 ◽

2018 ◽

Vol 144 (6) ◽

pp. 3475-3484

Author(s):

Xiangxia Meng ◽

Xiukun Li ◽

Andreas Jakobsson ◽

Yahui Lei

Keyword(s):

Sparse Estimation ◽

Underwater Object

Download Full-text

Sparse estimation from noisy observations of an overdetermined linear system

Automatica ◽

10.1016/j.automatica.2014.08.018 ◽

2014 ◽

Vol 50 (11) ◽

pp. 2845-2851 ◽

Cited By ~ 1

Author(s):

Liang Dai ◽

Kristiaan Pelckmans

Keyword(s):

Linear System ◽

Sparse Estimation ◽

Overdetermined Linear System

Download Full-text

Sparse Estimation for Structural Variability

Lecture Notes in Computer Science - Algorithms in Bioinformatics ◽

10.1007/978-3-642-15294-8_2 ◽

2010 ◽

pp. 13-27

Author(s):

Raghavendra Hosur ◽

Rohit Singh ◽

Bonnie Berger

Keyword(s):

Sparse Estimation ◽

Structural Variability

Download Full-text

Sparse Estimation and Uncertainty with Application to Subgroup Analysis

Political Analysis ◽

10.1017/pan.2016.14 ◽

2017 ◽

Vol 25 (1) ◽

pp. 1-40 ◽

Cited By ~ 13

Author(s):

Marc Ratkovic ◽

Dustin Tingley

Keyword(s):

Variable Selection ◽

Confidence Intervals ◽

Public Support ◽

Theoretical Work ◽

Bayesian Variable Selection ◽

Order Effects ◽

Selection Methods ◽

Sparse Estimation ◽

Recent Theoretical Work ◽

Subgroup Effects

We introduce a Bayesian method, LASSOplus, that unifies recent contributions in the sparse modeling literatures, while substantially extending pre-existing estimators in terms of both performance and flexibility. Unlike existing Bayesian variable selection methods, LASSOplus both selects and estimates effects while returning estimated confidence intervals for discovered effects. Furthermore, we show how LASSOplus easily extends to modeling repeated observations and permits a simple Bonferroni correction to control coverage on confidence intervals among discovered effects. We situate LASSOplus in the literature on how to estimate subgroup effects, a topic that often leads to a proliferation of estimation parameters. We also offer a simple preprocessing step that draws on recent theoretical work to estimate higher-order effects that can be interpreted independently of their lower-order terms. A simulation study illustrates the method’s performance relative to several existing variable selection methods. In addition, we apply LASSOplus to an existing study on public support for climate treaties to illustrate the method’s ability to discover substantive and relevant effects. Software implementing the method is publicly available in theRpackagesparsereg.

Download Full-text