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A varying-coefficient generalized odds rate model with time-varying exposure: An application to fitness and cardiovascular disease mortality

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BIOMETRICS 2019年第3期75卷 853-863页

作者： Zhou, Jie Zhang, Jiajia Mclain, Alexander C. Lu, Wenbin Sui, Xuemei Hardin, James W. Univ South Carolina Dept Epidemiol & Biostat Columbia SC 29208 USA North Carolina State Univ Dept Stat Raleigh NC USA Univ South Carolina Dept Exercise Columbia SC 29208 USA

Varying-coefficient models have become a common tool to determine whether and how the association between an exposure and an outcome changes over a continuous measure. These models are complicated when the exposure itself is time-varying and subjected to measurement error. For example, it is well known that longitudinal physical fitness has an impact on cardiovascular disease (CVD) mortality. It is not known, however, how the effect of longitudinal physical fitness on CVD mortality varies with age. In this paper, we propose a varying-coefficient generalized odds rate model that allows flexible estimation of age-modified effects of longitudinal physical fitness on CVD mortality. In our model, the longitudinal physical fitness is measured with error and modeled using a mixed-effects model, and its associated age-varying coefficient function is represented by cubic B-splines. An expectation-maximization algorithm is developed to estimate the parameters in the joint models of longitudinal physical fitness and CVD mortality. A modified pseudoadaptive Gaussian-Hermite quadrature method is adopted to compute the integrals with respect to random effects involved in the E-step. The performance of the proposed method is evaluated through extensive simulation studies and is further illustrated with an application to cohort data from the Aerobic Center Longitudinal Study.

关键词： B-splines expectation-maximization algorithm generalized odds rate model joint modeling varying coefficient

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Noise-boosted bidirectional backpropagation and adversarial learning

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NEURAL NETWORKS 2019年 120卷 9-31页

作者： Adigun, Olaoluwa Kosko, Bart Univ Southern Calif Dept Elect & Comp Engn Signal & Image Proc Inst Los Angeles CA 90089 USA

Bidirectional backpropagation trains a neural network with backpropagation in both the backward and forward directions using the same synaptic weights. Special injected noise can then improve the algorithm's training time and accuracy because backpropagation has a likelihood structure. Training in each direction is a form of generalized expectation-maximization because backpropagation itself is a form of generalized expectation-maximization. This requires backpropagation invariance in each direction: The gradient log-likelihood in each direction must give back the original update equations of the backpropagation algorithm. The special noise makes the current training signal more probable as bidirectional backpropagation climbs the nearest hill of joint probability or log-likelihood. The noise for injection differs for classification and regression even in the same network because of the constraint of backpropagation invariance. The backward pass in a bidirectionally trained classifier estimates the centroid of the input pattern class. So the feedback signal that arrives back at the input layer of a classifier tends to estimate the local pattern-class centroid. Simulations show that noise speeded convergence and improved the accuracy of bidirectional backpropagation on both the MNIST test set of hand-written digits and the CIFAR-10 test set of images. The noise boost further applies to regular and Wasserstein bidirectionally trained adversarial networks. Bidirectionality also greatly reduced the problem of mode collapse in regular adversarial networks. (C) 2019 Published by Elsevier Ltd.

关键词： Bidirectional backpropagation Neural networks Noise benefit Stochastic resonance expectation-maximization algorithm Bidirectional associative memory Noise benefit

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Prediction analysis for microbiome sequencing data

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BIOMETRICS 2019年第3期75卷 875-884页

作者： Wang, Tao Yang, Can Zhao, Hongyu Shanghai Jiao Tong Univ Dept Bioinformat & Biostat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China Shanghai Jiao Tong Univ SJTU Yale Joint Ctr Biostat & Data Sci Shanghai Peoples R China Hong Kong Univ Sci & Technol Dept Math Kowloon Hong Kong Peoples R China Yale Univ Dept Biostat New Haven CT USA

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension-reduction structure in the model. An efficient Monte Carlo expectation-maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.

关键词： expectation-maximization algorithm log ratios metagenomic data model-based dimension reduction multinomial-logit regression

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Optimal Multi-Objective Burn-In Policy Based on Time-Transformed Wiener Degradation Process

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IEEE ACCESS 2019年 7卷 73529-73539页

作者： Lyu, Yi Zhang, Yun Chen, Kairui Chen, Ci Zeng, Xianxian Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Univ Elect Sci & Technol China Sch Comp Zhongshan Inst Zhongshan 528400 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Burn-in is an effective and widely used means to improve product reliability by eliminating weak units before they are distributed in the market. Traditional burn-in that distinguishes weak units by failure during testing is inefficient and incompetent for degradation-failed products in which weak units degrade faster than normal individuals. Hence, the manufacturers have to turn to the degradation-based method. The mean lifetime to failure (MTTF) of a burnt-in population is diminished because of this type of burn-in increases the degradation level of all tested units. Ignoring the impact of burn-in leads to inferior test decisions. This study develops a multi-objective burn-in method that can simultaneously minimize the burn-in cost and maximize the burnt-in population's MTTF. We employ the time-transformed Wiener process with random effects to model the nonlinear degradation path of products and develop a burn-in scheme with two decision variables, namely, test duration and screening cutoff level. Cost expression and lifetime-based optimal objective are analytically developed. The optimal test policy is determined using the multi-objective evolutionary algorithm based on decomposition. A simulation study is conducted to demonstrate the usage and effectiveness of the multi-objective burn-in method.

关键词： Degradation Wiener process burn-in expectation-maximization algorithm multi-objective optimization

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Linear parameter-varying systems identification with slowly sampled outputs subjected to unknown time-delays and outliers

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2019年第1期233卷 5-17页

作者： Liu, Xin Yang, Xianqiang Guo, Feng Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Heilongjiang Peoples R China NanTong Inst Technol Nantong Peoples R China

This article proposes a robust global identification approach for nonlinear systems with slowly sampled outputs subjected to unknown time-delays. The inputs of the system are fast-rate sampled while the informative outputs are uniformly sampled at a slow rate. In practical industries, the outlier and output time-delays are commonly encountered and they are both considered in this work. To eliminate the impact brought by the outliers, the robust linear parameter-varying finite impulse response observation model based on the Laplace distribution is established. The unknown time-delay is treated as a random integer at each sampling moment which follows the uniform distribution with the boundary known a priori. The proposed robust approach is derived in the expectation-maximization algorithm scheme and the formulas to iteratively update the model parameters, the scale parameter and the random output time-delays are derived, respectively. The unmeasurable noise-free fast-rate sampled outputs are estimated by simulating the identified model. A numerical example, the mass-spring-damper system and the two-link robotic manipulator are utilized to verify the effectiveness of the proposed approach.

关键词： Robust global approach linear parameter-varying model identification random output time-delays Laplace distribution expectation-maximization algorithm

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A new multivariate zero-adjusted Poisson model with applications to biomedicine

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BIOMETRICAL JOURNAL 2019年第6期61卷 1340-1370页

作者： Liu, Yin Tian, Guo-Liang Tang, Man-Lai Yuen, Kam Chuen Zhongnan Univ Econ & Law Sch Stat & Math Wuhan 430073 Hubei Peoples R China Southern Univ Sci & Technol Dept Math Shenzhen 518055 Guangdong Peoples R China Hang Seng Management Coll Sch Decis Sci Dept Math & Stat Siu Lek YuenShatin Hong Kong Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Pokfulam Rd Hong Kong Peoples R China

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, 1989) cannot be used to fit multivariate count data with excess zero-vectors;(ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases;(iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods.

关键词： data augmentation algorithm expectation-maximization algorithm hypothesis testing multivariate zero-adjusted Poisson stochastic representation

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Reliability assessment of multi-state phased-mission systems by fusing observation data from multiple phases of operation

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年 118卷 603-622页

作者： Xu, Zhaoping Mo, Yuchang Liu, Yu Jiang, Tao Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu 611731 Sichuan Peoples R China Huaqiao Univ Fujian Prov Univ Key Lab Computat Sci Sch Math Sci Quanzhou 362021 Fujian Peoples R China

Multi-state phased-mission systems (MS-PMSs) are multi-state systems that intend to complete multiple, consecutive, and non-overlapping phases of operation with different functional requirements. Even though many computationally efficient tools have been developed to facilitate reliability modeling of MS-PMSs in the past few decades, reliability assessment of an MS-PMS by observation data is still a difficulty because of the state and phase dependencies. This paper devotes to addressing this challenge by putting forth a new method that can assess reliability of an MS-PMS by fusing observation data collected from multiple phases of operation. A dynamic Bayesian network (DBN) model is constructed to characterize the state dependence between an MS-PMS and its units as well as the phase dependence of each unit across consecutive phases. By putting observation data into the DBN model, the joint probability distributions of the nodes of the MS-PMS at any time slice can be updated, and with which the unknown parameters associated with the state transitions of all the units can be furtherly estimated by a tailored expectation-maximization (EM) algorithm. The reliability function of the MS-PMS can be, then, assessed by these estimates of all the units. Two illustrative examples, including a numerical case and a data collection robot are exemplified to demonstrate the effectiveness of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multi-state phased-mission systems Dynamic Bayesian network Observation data Reliability assessment expectation-maximization algorithm

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Speeding up parameter and rule learning for acyclic probabilistic logic programs

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2019年 106卷 32-50页

作者： de Faria, Francisco H. O. Vieira Gusmao, Arthur Colombini De Bona, Glauber Maua, Denis Deratani Cozman, Fabio Gagliardi Univ Sao Paulo Escola Politecn Sao Paulo Brazil Univ Sao Paulo Inst Matemat & Estat Sao Paulo Brazil

This paper introduces techniques that speed-up parameter and rule learning for acyclic probabilistic logic programs. We focus on maximum likelihood estimation of parameters, and show that significant improvements can be obtained by efficiently handling probabilistic rules. We then move to structure learning, where we learn sets of rules, by introducing an algorithm that greatly simplifies exact score-based learning. Experiments demonstrate that our methods can produce orders of magnitude speed-ups over the state-of-art in parameter and rule learning. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Probabilistic logic programming expectation-maximization algorithm Rule learning

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Inverse Gaussian-based model with measurement errors for degradation analysis

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2019年第6期233卷 1086-1098页

作者： Chen, Xudan Ji, Guoxun Sun, Xinli Li, Zhen Xian Res Inst High Tech Xian 710000 Shaanxi Peoples R China Naval Acad Beijing Peoples R China

To build more credible degradation models, it is necessary to consider measurement errors in degradation analysis. This article proposes an inverse Gaussian-based state space model with measurement errors that can capture the unit-to-unit variability of the degradation rate by incorporating a random effect. Then, the lifetime distribution and alarm probabilities are derived. Under the non-Gaussian assumptions, conventional parameter estimation algorithms cannot be applied directly. Therefore, an improved expectation-maximization algorithm that is combined with particle methods is developed to estimate parameters. Finally, this article concludes with a simulation study and two case applications to demonstrate the applicability and advantages of the proposed model.

关键词： State space model unit-to-unit variability alarm probabilities expectation-maximization algorithm particle methods

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Point Set Registration With a Hybrid Structure Constraint

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IEEE ACCESS 2019年 7卷 164246-164255页

作者： Sun, Jing Chen, Xia Sun, Zhan-Li Lam, Kin-Man Zeng, Zhigang Anhui Univ Sch Elect Engn & Automat Hefei 230039 Anhui Peoples R China Hefei ZC Optoelect Technol Ltd Anhui Prov Key Lab Nondestruct Evaluat Hefei 230031 Anhui Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Due to some unfavorable factors, how to accurately register point sets is still a challenging task. In this paper, an effective point set registration approach is proposed based on a hybrid structure constrain. In the proposed method, a composite weight coefficient is determined based on the amplitudes of the vector and the corresponding projection of neighbor points. Given the composite weight coefficient, a local structure constraint is constructed as a linear combination of the vectors of neighbor points. A Gaussian mixture model is established by utilizing the local structure constraint and a global structure constraint based on the motion coherence theory. In addition, an expectation-maximization algorithm is derived to solve the unknown variables in the proposed model. For the constraint terms, an update strategy is utilized to obtain the approximately optimal weight coefficients. Compared to the state-of-the-art approaches, the proposed model is more robust due to the use of multiple effective constraints. Experimental results on some widely used data sets demonstrate the effectiveness of the proposed model.

关键词： Point set registration Gaussian mixture model structure constraint expectation-maximization algorithm

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