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Penalized empirical likelihood for the sparse Cox regression model

JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2019年 201卷 71-85页

作者： Wang, Dongliang Wu, Tong Tong Zhao, Yichuan SUNY Upstate Med Univ Dept Publ Hlth & Prevent Med Syracuse NY 13210 USA Univ Rochester Dept Biostat & Computat Biol Rochester NY 14627 USA Georgia State Univ Dept Math & Stat Atlanta GA 30303 USA

The current penalized regression methods for selecting predictor variables and estimating the associated regression coefficients in the sparse Cox model are mainly based on partial likelihood. In this paper, a bias-corrected empirical likelihood method is proposed for the sparse Cox model in conjunction with appropriate penalty functions when the dimensionality of data is high. Theoretical properties of the resulting estimator for the large sample are proved. Simulation studies suggest that penalized empirical likelihood works better than partial likelihood in terms of selecting correct predictors without introducing more model errors. The well-known primary biliary cirrhosis data set is used to illustrate the proposed penalized empirical likelihood method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： coordinate descent algorithm Bias-corrected empirical likelihood High-dimensional data Oracle property Penalized likelihood Sparse proportional hazards model

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Robust Gaussian Graphical Modeling Via l₁ Penalization

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BIOMETRICS 2012年第4期68卷 1197-1206页

作者： Sun, Hokeun Li, Hongzhe Univ Penn Dept Biostat & Epidemiol Perelman Sch Med Philadelphia PA 19104 USA

Gaussian graphical models have been widely used as an effective method for studying the conditional independency structure among genes and for constructing genetic networks. However, gene expression data typically have heavier tails or more outlying observations than the standard Gaussian distribution. Such outliers in gene expression data can lead to wrong inference on the dependency structure among the genes. We propose a l(1) penalized estimation procedure for the sparse Gaussian graphical models that is robustified against possible outliers. The likelihood function is weighted according to how the observation is deviated, where the deviation of the observation is measured based on its own likelihood. An efficient computational algorithm based on the coordinate gradient descent method is developed to obtain the minimizer of the negative penalized robustified-likelihood, where nonzero elements of the concentration matrix represents the graphical links among the genes. After the graphical structure is obtained, we re-estimate the positive definite concentration matrix using an iterative proportional fitting algorithm. Through simulations, we demonstrate that the proposed robust method performs much better than the graphical Lasso for the Gaussian graphical models in terms of both graph structure selection and estimation when outliers are present. We apply the robust estimation procedure to an analysis of yeast gene expression data and show that the resulting graph has better biological interpretation than that obtained from the graphical Lasso.

关键词： coordinate descent algorithm Genetic network Iterative proportional fitting Outliers Penalized likelihood

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Customized algorithms for growing connected resistive networks

Customized algorithms for growing connected resistive networ...

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10th IFAC Symposium on Nonlinear Control Systems (NOLCOS)

作者： Moghaddam, Sepideh Hassan Juvanovic, Mihailo R. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

We consider the problem of adding edges to connected resistive networks in order to optimally enhance their performance. The performance is captured by the H-2 norm of the closed-loop network and the l(1) regularization is introduced as a means to promote sparsity of the controller graph Laplacian. The resulting optimal control problem can be used to officiently compute the optimal solution for small and medium size networks. In the paper, we develop two efficient customized algorithms for large-scale problems. Our customized algorthms are based on the proximal gradient method and the sequential quadratic approximation method. In the latter, the Newton direction is obtained using coordinate descent algorthm over the set of active varibles. We provide compricon of these methods and show that both of them can be effectively employed to solve topology identification and optimal design problem for large scale networks. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Convex optimization coordinate descent algorithm resistive networks proximal gradient method semidefinite programming sequential quadratic approximation

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COFADMM: A Computational features selection with Alternating Direction Method of Multipliers

COFADMM: A Computational features selection with Alternating...

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14th Annual International Conference on Computational Science

作者： El Anbari, Mohammed Alam, Sidra Bensmail, Halima Qatar Comp Res Ctr Doha Qatar Carnegie Mellon Univ Doha Qatar

Due to the explosion in size and complexity of Big Data, it is increasingly important to be able to solve problems with very large number of features. Classical feature selection procedures involves combinatorial optimization, with computational time increasing exponentially with the number of features. During the last decade, penalized regression has emerged as an attractive alternative for regularization and high dimensional feature selection problems. Alternating Direction Method of Multipliers (ADMM) optimization is suited for distributed convex optimization and distributed computing for big data. The purpose of this paper is to propose a broader algorithm COFADMM which combines the strength of convex penalized techniques in feature selection for big data and the power of the ADMM for optimization. We show that combining the ADMM algorithm with COFADMM can provide a path of solutions efficiently and quickly. COFADMM is easy to use, is available in C, Matlab upon request from the corresponding author.

关键词： features selection lasso least angle regression algorithm coordinate descent algorithm ADMM algorithm

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FILTERING SMOOTH ALTIMETRIC SIGNALS USING A BAYESIAN algorithm 24

FILTERING SMOOTH ALTIMETRIC SIGNALS USING A BAYESIAN ALGORIT...

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24th European Signal Processing Conference (EUSIPCO)

作者： Halimi, Abderrahim Buller, Gerald McLaughlin, Steve Honeine, Paul Heriot Watt Univ Sch Engn & Phys Sci Edinburgh Midlothian Scotland Normandie Univ UNIROUEN UNIHAVRE INSA RouenLITIS Rouen France

ISBN: (纸本)9780992862657

This paper presents a new Bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous signals that can have a numerical or an analytical expression with respect to some parameters. The Bayesian model proposed takes into account the Gaussian properties of the noise and the smooth evolution of the successive signals. In addition, a gamma Markov random field prior is assigned to the signal energies and to the noise variances to account for their known properties. The resulting posterior distribution is maximized using a fast coordinate descent algorithm whose parameters are updated by analytical expressions. The proposed algorithm is tested on satellite altimetric data demonstrating good denoising results on both synthetic and real signals. The proposed algorithm is also shown to improve the quality of the altimetric parameters when combined with a parameter estimation strategy.

关键词： Altimetry Bayesian algorithm coordinate descent algorithm gamma Markov random fields

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A new methodology for evaluating profile and position errors of blade based on parameter priority

A new methodology for evaluating profile and position errors...

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International Conference on Optical Instruments and Technology (OIT) - Optoelectronic Measurement Technology and Systems

作者： Hao, Junjie Huang, Junhui Wang, Zhao Gao, Janmin Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Western China Inst Qual Sci & Technol Xian 710049 Peoples R China

ISBN: (纸本)9781510636576

As one of the core components of a turbine, the quality of the blade manufacturing has a strong impact on the energy conversion efficiency of the turbine, where the key technology of quality evaluation of blades is point cloud registration. However, with the application of structured light three-dimensional measurement technology in full profile measurement, the typical point cloud registration methods only focus on the minimization of surface profile error, ignoring the position error relative to reference datum, which can easily lead to the misjudgment of qualified blades. In this paper, a new blade error evaluation method is presented to register the point cloud data scanned from a physical blade to its theoretical CAD model, which fits the two surfaces based on parameter priority. Firstly, qualified blades are quickly selected after global fine registration using the best-fit algorithm. Subsequently, based on the priority of position error parameters, the coordinate descent algorithm combined with the minimum zone criterion is adopted for local fine registration, which guarantees accurate evaluation results. Finally, the shape and position error of the actual blade is obtained accurately by calculating transformation parameters of registration and the deviation between the registered point cloud and its CAD model of the blade. Experimental results show that compared with state-of-the-art registration methods, the presented method gives higher priority to the parameters which are difficult to finish or repair by machining, and the position errors are controlled in the tolerance area, which effectively reduces the misjudgment. In addition, evaluation results of blade errors with the method are mainly reflected in profile, which is valuable for guiding the blade finishing or trimming in practice.

关键词： Structured light Point cloud registration Shape and position error coordinate descent algorithm Blade surface inspection

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SPARSE MODELING ON DISTRIBUTED ENCRYPTION DATA

SPARSE MODELING ON DISTRIBUTED ENCRYPTION DATA

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bandoh, Yukihiro Nakachi, Takayuki Kiya, Hiroshi NTT Corp Yokosuka Kanagawa 2390847 Japan Tokyo Metropolitan Univ Tokyo 1910065 Japan

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

Big-data analysis by edge/cloud systems is becoming more important. However, when information may lead to personal identification, such information tends to be encrypted and restricted to its owners to ensure privacy protection. The resulting data is often insufficiently detailed to permit useful analysis. As a result, the desired analysis accuracy may not be achieved. To deal with this issue, several studies have examined encryptions based on the random unitary transform. This is because the random unitary transform has lower computational complexity than other encryption schemes, and its encryption domain supports several signal processing algorithms. However, analysis models on distributed encrypted data, have not been studied deeply enough. In this paper, we construct an analysis model for data encrypted with the random unitary transform by deriving a LASSO solution for encrypted data. The analytical model can derive the same LASSO solution as that yielded by processing the original data (i.e. without encryption). The analytical model supports distributed encryption, where a data set consists of different components that are encrypted at different sites independently. The collaboration enables us to improve the accuracy of analysis for distributed privacy-sensitive information.

关键词： LASSO coordinate descent algorithm random unitary transform

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AN ITERATIVELY WEIGHTED MMSE APPROACH TO DISTRIBUTED SUM-UTILITY MAXIMIZATION FOR A MIMO INTERFERING BROADCAST CHANNEL

AN ITERATIVELY WEIGHTED MMSE APPROACH TO DISTRIBUTED SUM-UTI...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shi, Qingjiang Razaviyayn, Meisam Luo, Zhi-Quan He, Chen Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200030 Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781457705397

Consider the MIMO interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to the users in other cells. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean squared error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, we extend the algorithm to a general class of utility functions and establish its convergence. The resulting algorithm can be implemented in a distributed asynchronous manner. The effectiveness of the proposed algorithm is validated by numerical experiments.

关键词： MIMO Interfering Broadcast Channel Power Allocation Beamforming coordinate descent algorithm

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Spatio-Temporal Adaptive Fused Lasso for Proportion Data 13th

Spatio-Temporal Adaptive Fused Lasso for Proportion Data

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13th International KES Conference on Intelligent Decision Technologies (KES-IDT)

作者： Yamamura, Mariko Ohishi, Mineaki Yanagihara, Hirokazu Radiat Effects Res Fdn Dept Stat Minami Ku 5-2 Hijiyama Pk Hiroshima 7320815 Japan Hiroshima Univ Educ & Res Ctr AI & Data Innovat Naka Ku 1-1-89 Higashi Senda Hiroshima 7300053 Japan Hiroshima Univ Grad Sch Adv Sci & Engn 1-3-1 Kagamiyama Higashihiroshima Hiroshima 7398526 Japan

ISBN: (纸本)9789811627651;9789811627644

Population-corrected rates are often used in statistical documents that show the features of a municipality. In addition, it is important to determine changes of the features over time, and for this purpose, data collection is continually carried out by census. In the present study, we propose a method for analyzing the spatiotemporal effects on rates by adaptive fused lasso. For estimation, the coordinate descent algorithm, which is known to have better estimation accuracy and speed than the algorithm used in genlasso in the R software package, is used for optimization. Based on the results of the real data analysis for the crime rates in the Kinki region of Japan in 1995-2008, the proposed method can be applied to spatio-temporal proportion data analysis.

关键词： Spatio-temporal proportion data Adaptive fused lasso coordinate descent algorithm

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K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting

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INTERNATIONAL JOURNAL OF FORECASTING 2016年第3期32卷 1074-1080页

作者： Zhang, Yao Wang, Jianxue Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China

Probabilistic forecasts provide quantitative information in relation to energy uncertainty, which is essential for making better decisions on the operation of power systems with an increasing penetration of wind power. On the basis of the k-nearest neighbors algorithm and a kernel density estimator method, this paper presents a general framework for the probabilistic forecasting of renewable energy generation, especially for wind power generation. It is a direct and non-parametric approach. Firstly, the k-nearest neighbors algorithm is used to find the k closest historical examples with characteristics similar to the future weather condition of wind power generation. Secondly, a novel kernel density estimator based on a logarithmic transformation and a boundary kernel is used to construct wind power predictive density based on the k closest historical examples. The effectiveness of this approach has been confirmed on the real data provided for GEFCom2014. The evaluation results show that the proposed approach can provide good quality, reliable probabilistic wind power forecasts. (C) 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

关键词： Wind power Probabilistic forecasting Point forecasting k-nearest neighbors Kernel density estimator coordinate descent algorithm

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