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4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Jaoua, Nouha Vanheeghe, Philippe Navarro, Nicolas Langlois, Olivier Iordache, Marius IRT Railenium Valenciennes France Univ Lille CNRS Cent Lille UMR CRIStAL 9189 Lille France Alstom St Ouen France

ISBN: (纸本)9781538652398

In this paper, we address the problem of parameter estimation in railway systems. For this purpose, a physical model of the train based on the fundamental principle of dynamics is proposed. Then, the parameter estimation is handled via an approach using a combination of expectation-maximization algorithm and Sequential Monte Carlo methods. The experiments performed both on synthetic and real data show the efficiency of the considered method.

关键词： parameter estimation expectation-maximization algorithm Sequential Monte Carlo methods railway systems

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An Adaptive EM algorithm for NHPP Software Reliability Models 61

An Adaptive EM Algorithm for NHPP Software Reliability Model...

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61st Annual Reliability and Maintainability Symposium (RAMS)

作者： Nagaraju, Vidhyashree Fiondella, Lance Univ Massachusetts Dartmouth Dept Elect & Comp Engn 285 Old Westport Rd N Dartmouth MA 02747 USA

ISBN: (纸本)9781479967032

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGM) enable several quantitative metrics that can be used to guide important decisions during the software engineering life cycle such as testing resource allocation and release planning. However, many of these SRGM possess complex mathematical forms that make them difficult to apply in practice because traditional statistical procedures such as maximum likelihood estimation must solve a system of non-linear equations to identify the numerical parameters that best characterize a set of failure data. Recently, researchers have made significant progress in overcoming this difficulty by developing an expectation-maximization (EM) algorithm that exhibits better convergence properties and can therefore find the maximum likelihood estimates of complex SRGM with greater ease. This EM algorithm, however, assumes that some model parameters are constant and thus the approach is not capable of identifying the set of numerical parameters that maximize the likelihood function. This paper presents an adaptive EM algorithm to identify the maximum likelihood estimates of all parameters of multiple NHPP SRGM with complex mathematical forms. We illustrate our enhanced algorithm through a series of examples. The results show that the algorithm can efficiently identify the set of numerical parameters that globally maximizes the likelihood function. Thus, the adaptive algorithm can significantly simplify the application of complex SRGM.

关键词： expectation-maximization algorithm Non-homogeneous Poisson process Software reliability

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An approach to segment lung pleura from CT data with high precision

An approach to segment lung pleura from CT data with high pr...

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Medical Imaging 2008 Conference

作者： Company, E. Angelats Chalsaowong, K. Knepper, A. Kraus, T. Aach, T. Rhein Westfal TH Aachen Inst Imaging & Comp Vis Templergraben 55 D-52056 Aachen Germany King Mongkuts Inst Technol North Bangkok Sirindhorn Int Thai German Grad Sch Engn Bangkok 10800 Thailand Univ Hosp Aachen Inst & Out Patient Clin Occupational Med D-52074 Aachen Germany

ISBN: (纸本)9780819470980

A new approach to segment pleurae from CT data with high precision is introduced. This approach is developed in the segmentation's framework of an image analysis system to automatically detect pleural thickenings. The new technique to carry out the 3D segmentation of lung pleura is based on supervised range-constrained thresholding and a Gibbs-Markov random field model. An initial segmentation is done using the 3D histogram by supervised range-constrained thresholding. 3D connected component labelling is then applied to find the thorax. In order to detect and remove trachea and bronchi therein, the 3D histogram of connected pulmonary organs is modelled as a finite mixture of Gaussian distributions. Parameters are estimated using the expectation-maximization algorithm, which leads to the classification of that pulmonary region. As consequence left and right lungs are separated. Finally we apply a Gibbs-Markov random field model to our initial segmentation in order to achieve a high accuracy segmentation of lung pleura. The Gibbs-Markov random field is combined with maximum a posteriori estimation to estimate optimal pleural contours. With these procedures, a new segmentation strategy is developed in order to improve the reliability and accuracy of the detection of pleural contours and to achieve a better assessment performance of pleural thickenings.

关键词： malignant pleural mesothelioma pleural thickening segmentation supervised range-constrained thresholding Gaussian mixture model expectation-maximization algorithm Gibbs-Markov random field maximum a posteriori estimation

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Data-Driven Statistical Analysis of Dynamic Vessel Trajectories in Wuhan Section of the Yangtze River 17

Data-Driven Statistical Analysis of Dynamic Vessel Trajector...

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International Conference on Big Data Research (ICBDR)

作者： Liang, Maohan Liu, Ryan Wen Li, Yan Wu, Jianhua Liu, Jingxian Wuhan Univ Technol Sch Nav Hubei Key Lab Inland Shipping Technol Wuhan 430063 Hubei Peoples R China

ISBN: (纸本)9781450353564

Automatic identification system (AIS), which records the spatio-temporal dynamic vessel trajectories, has recently attracted increasing attention due to its great potential in maritime management and ocean engineering. AIS has been successfully utilized to assist intelligent navigation and enhance transportation safety. To better understand the vessel behavior behind the massive AIS trajectories, it is necessary to analyze the statistical properties of AIS trajectories from different aspects. In this work, we mainly focus on the distribution of vessel speeds, distribution of spherical distances, distribution of longitude and latitude differences between successive trajectory points for both downstream and upstream vessels in Wuhan Section of the Yangtze River. To accurately approximate these distributions, Gaussian mixture model (GMM) was introduced to analyze the dynamic vessel trajectories. In particular, the optimal parameters of GMM were estimated using the iterative expectation-maximization (EM) algorithm. Experiments on massive realistic vessel trajectories have demonstrated that there exists significantly different distributions of vessel speeds, spherical distances, longitude and latitude differences between downstream and upstream vessels.

关键词： Trajectory mining Automatic identification system Gaussian mixture model expectation-maximization algorithm Statistical analysis

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An Analysis of the Effect of Streaming on Civic Participation Through a Causal Hidden Markov Model

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SOCIAL INDICATORS RESEARCH 2024年第1期172卷 163-190页

作者： Bartolucci, Francesco Favaro, Donata Pennoni, Fulvia Sciulli, Dario Univ Perugia Dept Econ Via A Pascoli 20 I-06123 Perugia Italy Univ Padua Dept Econ & Management Via Santo 33 I-35123 Padua Italy Univ Milano Bicocca Dept Stat & Quantitat Methods Via Bicocca Arcimboldi 8 I-20126 Milan Italy Univ G dAnnunzio Dept Econ Studies Viale Pindaro 42 I-65127 Pescara Italy

We examine the effect of streaming based on ability levels on individuals' civic participation throughout their adult life. The hypothesis we test is that ability grouping influences individuals' general self-concept and, consequently, their civic participation choices across the life course. We employ data from the British National Child Development Study, which follows all UK citizens born during a certain week in 1958. Six binary variables observed at 33, 42, and 51 years of age are considered to measure civic participation. Our approach defines causal estimands with multiple treatments referring to the evolution of civic engagement over time in terms of potential versions of a sequence of latent variables assumed to follow a Markov chain with initial and transition probabilities depending on posttreatment time-varying covariates. The model also addresses partially or entirely missing data on one or more indicators at a given time occasion and missing posttreatment covariate values using dummy indicators. The model is estimated by maximizing a weighted log-likelihood function with weights corresponding to the inverse probability of the received treatment obtained from a multinomial logit model based on pretreatment covariates. Our results show that ability grouping affects the civic participation of high-ability individuals when they are 33 years old with respect to participation in general elections.

关键词： Causal inference Civic participation expectation-maximization algorithm Generalized propensity score Voting behavior Streaming C31 C33 I21

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STOCHASTIC REVERBERATION MODEL WITH A FREQUENCY DEPENDENT ATTENUATION

STOCHASTIC REVERBERATION MODEL WITH A FREQUENCY DEPENDENT AT...

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

作者： Aknin, Achille Badeau, Roland Inst Polytech Paris Telecom Paris LTCI Palaiseau France

ISBN: (纸本)9781665448703

In various audio signal processing applications, such as source separation and dereverberation, accurate mathematical modeling of both source signals and room reverberation is needed to properly describe the audio data. In a previous paper, we introduced a stochastic room impulse response model based on the image source principle, and we proposed an expectation-maximization algorithm that was able to efficiently estimate the model parameters in various experimental settings. This paper aims to extend the model in order to account for the dependency of the exponential decay over frequency, due to the walls usually absorbing less energy at low frequencies than at high frequencies. Our experimental results show that this refinement of the model is able to generate realistic room impulse responses, that are perceptively very close to the original ones.

关键词： Reverberation room impulse response probabilistic modeling expectation-maximization algorithm artificial reverberation

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A spatial copula interpolation in a random field with application in air pollution data

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2023年第1期9卷 175-194页

作者： Thakur, Debjoy Das, Ishapathik Chakravarty, Shubhashree Indian Inst Technol Dept Math & Stat Tirupati Andhra Pradesh India Indian Stat Inst Machine Intelligence Unit Kolkata India

Interpolating a skewed conditional spatial random field with missing data is cumbersome in the absence of Gaussianity assumptions. Copulas can capture different types of joint tail characteristics beyond the Gaussian paradigm. Maintaining spatial homogeneity and continuity around the observed random spatial point is also challenging. Especially when interpolating along a spatial surface, the boundary points also demand focus in forming a neighborhood. As a result, importing the concept of hierarchical clustering on the spatial random field is necessary for developing the copula model with the interface of the expectation-maximization algorithm and concurrently utilizing the idea of the Bayesian framework. This article introduces a spatial cluster-based C-vine copula and a modified Gaussian distance kernel to derive a novel spatial probability distribution. To make spatial copula interpolation compatible and efficient, we estimate the parameter by employing different techniques. We apply the proposed spatial interpolation approach to the air pollution of Delhi as a crucial circumstantial study to demonstrate this newly developed novel spatial estimation technique.

关键词： Von-Mises distribution expectation-maximization algorithm Hierarchical Spatial Clustering Spatial Copula Interpolation Bayesian Spatial Copula Interpolation

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Choice model analysis of the "pick any/n" type of binary data

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JAPANESE PSYCHOLOGICAL RESEARCH 1998年第1期40卷 31-39页

作者： Takane, Y McGill Univ Dept Psychol Montreal PQ H3A 1B1 Canada

In the "pick any/n" method, subjects are asked to choose any number of items from a list of n items according to some criterion. This kind of data can be analyzed as a special case of either multiple-choice data or successive categories data where the number of response categories is limited to two. An item response model was proposed for the latter case, which is a combination of an unfolding model and a choice model. The marginal maximum-likelihood estimation method was developed for parameter estimation to avoid incidental parameters, and an expectation-maximization algorithm used for numerical optimization. Two examples of analysis are given to illustrate the proposed method, which we call MAXSC.

关键词： multiple-choice data successive categories data unfolding model marginal maximum-likelihood (MML) method expectation-maximization algorithm

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Recent developments in iterative image reconstruction for PET and SPECT

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2000年第4期19卷 257-260页

作者： Leahy, R Byrne, C Univ So Calif Dept Elect Engn Syst Los Angeles CA 90089 USA Univ So Calif Inst Signal & Image Proc Los Angeles CA 90089 USA Univ So Calif Dept Radiol Los Angeles CA USA Univ So Calif Dept Biomed Engn Los Angeles CA 90089 USA

Introduces a series of articles on the developments in iterative image reconstruction for positron-emission tomography (PET) and single proton emission computer tomography (SPECT).

关键词： expectation-maximization algorithm Emission tomography Maximum-likelihood Em algorithm Bayesian reconstruction Transmission tomography Ordered subsets Projections Systems Art Arts expectation-maximisation algorithm maximum likelihood image reconstruction single photon emission computed tomography Tomography, X-Ray emission tomography

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Model-Based Discriminant Analysis of High-Dimensional Data

Model-Based Discriminant Analysis of High-Dimensional Data

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作者： Mingzhu Sun The University of Queensland

学位级别：博士

This thesis addresses two important problems in modern statistics: discriminant analysis of big data and dimension reduction of high-dimensional data such as microarray gene expression data. These problems are commonly encountered in various scientific fields and can pose considerable challenges since traditional approaches might not work properly or even break down in the high-dimensional setting. For the first problem of discriminant analysis of big data, one of the widely used parametric approaches is to model the distribution of the feature vector in each of the predefined classes via a normal mixture distribution. The component-covariance matrices in the normal mixture for a class are highly parameterized, thus, rendering them impractical for high-dimensional datasets. Therefore, as the dimension increases, some forms of regularization need to be implemented. In this thesis, an innovative factor model approach, called mixtures of common factor ana- lyzers for discriminant analysis (MCFDA), is proposed. With this approach, the component- covariance matrices are taken to have a factor-analytic form with common loadings across the classes (common before the transformation of the factors into white noise). This approach also allows the data to be viewed in low-dimensional spaces by plotting the (estimated) values of the latent factors corresponding to the observed data points. To improve the robustness of our MCFDA approach for data which have heavy tails or atypical observations, we also adopt the multivariate t-family for the component-error and factor distri- butions. We refer to this model as the mixtures of common t-factor analyzers for discriminant analysis (MCtFDA). With this approach, both the common factor loadings and the diagonal matrix of error terms need to be specified as the same across the classes. This approach has great flexibility for modelling data which are non-normal or with outliers. For the second problem of dimension reduction, we focus on

关键词： finite mixture models discriminant analysis clustering microarray gene expression data expectation-maximization algorithm factor analysis model dimension reduction machine learning error rates

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