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Change Point Detection by sparse parameter estimation

INFORMATICA 2011年第1期22卷 149-164页

作者： Neubauer, Jiri Vesely, Vitezslav Univ Def Brno 61200 Czech Republic Masaryk Univ Brno 60200 Czech Republic

The contribution is focused on change point detection in a one-dimensional stochastic process by sparse parameter estimation from an overparametrized model. A stochastic process with change in the mean is estimated using dictionary consisting of Heaviside functions. The basis pursuit algorithm is used to get sparse parameter estimates. The mentioned method of change point detection in a stochastic process is compared with several standard statistical methods by simulations.

关键词： change point detection overparametrized model sparse parameter estimation

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Bayesian Learning-Based Linear Decentralized sparse parameter estimation in MIMO Wireless Sensor Networks Relying on Imperfect CSI

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第9期69卷 6236-6250页

作者： Rajput, Kunwar Pritiraj Kumar, Abhishek Srivastava, Suraj Jagannatham, Aditya K. Hanzo, Lajos Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India Kumar Qualcomm India Private Ltd Hyderabad 500081 India Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). Our proposed transceiver designs rely on majorization theory and hyperparameter estimates obtained from the BL module for minimizing the mean square error (MSE) of parameter estimation at the fusion center (FC). The linear transceiver design framework is initially proposed for the general scenario with arbitrary SNR sensor observations, followed by a special case with high-SNR sensor observations scenario. Our analysis also incorporates the channel correlation. The MMSE channel estimates are determined for the sensors (SNs), followed by a robust transceiver design procedure that is resilient to the channel state information (CSI) uncertainty arising due to the channel estimation error, an aberration that is unavoidable in practical implementations. Our simulation results demonstrate the improved performance of the proposed BL framework and optimal MMSE transceiver design in sparse parameter estimation relying on realistic imperfect channel estimates over the benchmarks.

关键词： estimation parameter estimation Channel estimation Wireless sensor networks MIMO communication Transceivers Uncertainty Sensor networks multiple access channel (MAC) Bayesian learning (BL) decentralized estimation sparse parameter estimation transceiver design stochastic CSI uncertainty

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New Method of sparse parameter estimation in Separable Models and Its Use for Spectral Analysis of Irregularly Sampled Data

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第1期59卷 35-47页

作者： Stoica, Petre Babu, Prabhu Li, Jian Uppsala Univ Dept Informat Technol SE-75105 Uppsala Sweden Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

Separable models occur frequently in spectral analysis, array processing, radar imaging and astronomy applications. Statistical inference methods for these models can be categorized in three large classes: parametric, nonparametric (also called "dense") and semiparametric (also called "sparse"). We begin by discussing the advantages and disadvantages of each class. Then we go on to introduce a new semiparametric/sparse method called SPICE (a semiparametric/sparse iterative covariance-based estimation method). SPICE is computationally quite efficient, enjoys global convergence properties, can be readily used in the case of replicated measurements and, unlike most other sparse estimation methods, does not require any subtle choices of user parameters. We illustrate the statistical performance of SPICE by means of a line-spectrum estimation study for irregularly sampled data.

关键词： Irregular sampling separable models sparse parameter estimation spectral analysis

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Connection between SPICE and Square-Root LASSO for sparse parameter estimation

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SIGNAL PROCESSING 2014年 95卷 10-14页

作者： Babu, Prabhu Stoica, Petre HKUST Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China Uppsala Univ Dept Informat Technol SE-75105 Uppsala Sweden

In this note we show that the sparse estimation technique named Square-Root LASSO (SR-LASSO) is connected to a previously introduced method named SPICE. More concretely we prove that the SR-LASSO with a unit weighting factor is identical to SPICE. Furthermore we show via numerical simulations that the performance of the SR-LASSO changes insignificantly when the weighting factor is varied. SPICE stands for sparse iterative covariance-based estimation and LASSO for least absolute shrinkage and selection operator. (C) 2013 Elsevier B.V. All rights reserved.

关键词： LASSO Square-Root LASSO SPICE Covariance fitting sparse parameter estimation

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Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation

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SIGNAL PROCESSING 2023年第1期213卷

作者： Babu, Prabhu Stoica, Petre Indian Inst Technol Ctr Appl Res Elect Delhi 110016 India Uppsala Univ Dept Informat Technol Div Syst & Control S-75237 Uppsala Sweden

We consider the problem of model selection for high-dimensional sparse linear regression models. We pose the model selection problem as a multiple-hypothesis testing problem and employ the methods of false discovery rate (FDR) and familywise error rate (FER) to solve it. We also present the reformulation of the FDR/FER-based approaches as criterion-based model selection rules and establish their relation to the extended Bayesian Information Criterion (EBIC), which is a state-of-the-art high-dimensional model selection rule. We use numerical simulations to show that the proposed FDR/FER method is well suited for high-dimensional model selection and performs better than EBIC.

关键词： Model selection sparse parameter estimation Mulitple hypothesis testing FDR FER

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SPICE: A sparse Covariance-Based estimation Method for Array Processing

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第2期59卷 629-638页

作者： Stoica, Petre Babu, Prabhu Li, Jian Uppsala Univ Dept Informat Technol SE-75105 Uppsala Sweden Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

This paper presents a novel sparse Iterative Covariance-based estimation approach, abbreviated as SPICE, to array processing. The proposed approach is obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many-snapshot cases but can be used even in single-snapshot situations. SPICE has several unique features not shared by other sparse estimation methods: it has a simple and sound statistical foundation, it takes account of the noise in the data in a natural manner, it does not require the user to make any difficult selection of hyperparameters, and yet it has global convergence properties.

关键词： Array processing covariance fitting direction-of-arrival (DOA) estimation sparse parameter estimation

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Robust air velocity estimation based on iterative implementation of Minimum Mean-Square Error (MMSE)

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AEROSPACE SCIENCE AND TECHNOLOGY 2015年 41卷 167-174页

作者： Yu, F. Tao, J. W. Yang, Y. M. Naval Aeronaut & Astronaut Univ Dept Control Engn Yantai 264001 Peoples R China Aviat Univ Dept Aircraft Control Engn Changchun 130022 Peoples R China Aviat Univ Dept Aviat Theory Changchun 130022 Peoples R China

The problem of transonic air velocity estimation based on acoustic sensor array is addressed. According to the propagation property of acoustic waves transmitted in subsonic or supersonic air current, an observation model of linearly acoustic sensor array is established for a given measuring equipment. Then, a robust air velocity estimator, abbreviated as II-MMSE, is proposed based on the iterative implementation of Minimum Mean-Square Error (MMSE) criterion. As a sparse estimation method, II-MMSE takes account of the noise covariance information in the array observation data in a natural manner, thus it does not require the users to make any choice of hyperparameters. Additionally, the II-MMSE technique is computationally more efficient than the generally hyperparametric sparse estimation methods, and it yields good robustness to limited snapshots and array modeling errors. Simulations are implemented to show the efficacy of II-MMSE. (C) 2015 Elsevier Masson SAS. All rights reserved.

关键词： Air velocity estimation Acoustic sensor array Minimum Mean-Square Error (MMSE) Array signal processing sparse parameter estimation

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Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks

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COMPUTERS & CHEMICAL ENGINEERING 2016年 90卷 111-120页

作者： Jang, Hong Kim, Kwang-Ki K. Braatz, Richard D. Gopaluni, R. Bhushan Lee, Jay H. Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn 291 Daehak Ro Taejon 305701 South Korea Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA MIT Dept Chem Engn Cambridge MA 02139 USA Univ British Columbia Dept Chem & Biol Engn 2360 East Mall Vancouver BC V6T 1Z3 Canada

A sparse parameter matrix estimation method is proposed for identifying a stochastic monomolecular biochemical reaction network system. Identification of a reaction network can be achieved by estimating a sparse parameter matrix containing the reaction network structure and kinetics information. Stochastic dynamics of a biochemical reaction network system is usually modeled by a chemical master equation (CME) describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular reaction systems for which an exact analytical solution of the corresponding chemical master equation can be derived. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized. A simulation result is provided to verify performance improvement of regularized MLE over least-square estimation (LSE), which is based on a deterministic mass-average model, in the case of a small population size. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： sparse parameter estimation Regularized maximum likelihood estimation Mono-molecular biochemical reaction network Chemical master equation Stochastic simulation algorithm

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A Note on the SPICE Method

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2013年第18期61卷 4545-4551页

作者： Rojas, Cristian R. Katselis, Dimitrios Hjalmarsson, Hakan KTH Royal Inst Technol Automat Control Lab Sch Elect Engn S-10044 Stockholm Sweden KTH Royal Inst Technol ACCESS Linnaeus Ctr Sch Elect Engn S-10044 Stockholm Sweden

In this article, we analyze the SPICE method developed in [1], and establish its connections with other standard sparse estimation methods such as the Lasso and the LAD-Lasso. This result positions SPICE as a computationally efficient technique for the calculation of Lasso-type estimators. Conversely, this connection is very useful for establishing the asymptotic properties of SPICE under several problem scenarios and for suggesting suitable modifications in cases where the naive version of SPICE would not work.

关键词： Irregular sampling sparse parameter estimation spectral analysis

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Efficient direction of arrival estimation based on sparse covariance fitting criterion with modeling mismatch

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SIGNAL PROCESSING 2017年 137卷 264-273页

作者： Cai, Shu Wang, Gang Zhang, Jun Wong, Kai-Kit Zhu, Hongbo Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing Jiangsu Peoples R China Ningbo Univ Fac Elect Engn & Comp Sci Ningbo Zhejiang Peoples R China UCL Dept Elect & Elect Engn London WC1E 7JE England

This paper studies direction of arrival (DoA) estimation with an antenna array using sparse signal reconstruction (SSR). Among the existing SSR methods, the sparse covariance fitting based algorithms, which can estimate source power and noise variance naturally, are most promising. Nevertheless, they are either on-grid model based methods whose performance are sensitive to off-grid DoAs or gridless methods which are computationally demanding. In this paper, we propose an off-grid DoA estimation algorithm based on the sparse covariance fitting criterion. We first consider a scenario in which the number of snapshots is larger than the array size. An algorithm is proposed by applying an off-grid model, which takes into account the deviations between the discretized sampling grid and the true DoAs, to the sparse covariance fitting criterion. It estimates the on-grid parameters and the deviations of off-grid DoAs separately and thus is computationally efficient to implement. Then in the case where the number of snapshots is smaller than the array size, we propose to execute the DoA estimation algorithm iteratively under the stochastic maximum likelihood (SML) criterion. The estimation accuracy and computational efficiency of the proposed algorithms are demonstrated by computer simulations. (C) 2017 Elsevier B.V. All rights reserved.

关键词： DoA estimation sparse parameter estimation Off-grid model sparse covariance fitting

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