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Multi-state models and missing covariate data: expectation–maximization algorithm for likelihood estimation

Biostatistics and Epidemiology 2017年第1期1卷 20-35页

作者： Lou, Wenjie Wan, Lijie Abner, Erin L. Fardo, David W. Dodge, Hiroko H. Kryscio, Richard J. Department of Statistics University of Kentucky Lexington KY United States Department of Biostatistics University of Kentucky Lexington KY United States Sanders-Brown Center on Aging University of Kentucky Lexington KY United States Department of Epidemiology University of Kentucky Lexington KY United States Department of Neurology C. Rex and Ruth H. Layton Center for Alzheimer's Research Oregon Health and Science University Portland OR United States Department of Neurology Michigan Alzheimer's Disease Center University of Michigan Ann Arbor MI United States

Multi-state models have been widely used to analyse longitudinal event history data obtained in medical and epidemiological studies. The tools and methods developed recently in this area require completely observed data. However, missing data within variables of interest are very common in practice, and they have been an issue in applications. We propose a type of expectation–maximization (em) algorithm, which handles missingness within multiple binary covariates efficiently, for multi-state model applications. Simulation studies show that the em algorithm performs well for both missing completely at random and missing at random covariate data. We apply the method to a longitudinal aging and cognition study data-set, the Klamath Exceptional Aging Project, whose data were collected at Oregon Health & Science University and integrated into the Statistical Models of Aging and Risk of Transition database at the University of Kentucky. © 2017, © 2017 International Biometric Society–Chinese Region.

关键词： em algorithm MAR MCAR missing covariates Multi-state model

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Statistical inference for zero-and-one-inflated poisson models

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Statistical Theory and Related Fields 2017年第2期1卷 216-226页

作者： Yincai Tang Wenchen Liu Ancha Xu School of Statistics East China Normal UniversityShanghaiChina College of Mathematics and Information Science Wenzhou UniversityZhejiangChina

In this paper, a zero-and-one-inflated Poisson (ZOIP) model is studied. The maximum likelihoodestimation and the Bayesian estimation of the model parameters are obtained based on dataaugmentation method. A simulation study based on proposed sampling algorithm is conductedto assess the performance of the proposed estimation for various sample sizes. Finally, two realdata-sets are analysed to illustrate the practicability of the proposed method.

关键词： Zero-inflated Poisson model zero-and-one-inflated Poisson model MLE Bayesian estimate em algorithm latent variable Gibbs sampling

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Identification of dynamical systems population described by a mixed effect ARX model structure

Identification of dynamical systems population described by ...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Batista, Levy Djermoune, El Hadi Bastogne, Thierry Telecom Nancy CYBERnano 193 Av Paul Muller F-54602 Villers Les Nancy France CNRS CRAN UMR 7039 BP 70239 F-54506 Vandoeuvre Les Nancy France BIGS INRIA BP 70239 F-54506 Vandoeuvre Les Nancy France

System identification is a data-driven input-output modeling approach more and more used in biology and biomedicine. In this application context, several assays are repeated to estimate the response variability and reproducibility. The inference of the modeling conclusions to the whole population requires to account for the inter-individual variability within the modeling procedure. One solution consists of using mixed effects models but up to now no similar approach exists in the system identification literature. In this article, we propose a first solution based on an ARX (Auto Regressive model with eXternal inputs) structure using the em (Expectation-Maximisation) algorithm for the estimation of the model parameters. Using the Fisher information matrix, the parameter standard errors are estimated;this allows for group comparison tests. Simulations show the relevance of this solution compared with a classical procedure of system identification repeated on each subject. Taking into account all the information available in the population allows to gather the parameters between individuals. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： System identification Mixed effects models em algorithm ARX structure bio-signals analysis

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Application of Inverse Gaussian Model in Mechanical Product Degradation Model 2

Application of Inverse Gaussian Model in Mechanical Product ...

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2nd International Conference on System Reliability and Safety (ICSRS)

作者： Sun, Bo Xu, Binbin He, Jialong Chen, Fei Yang, Zhaojun Liu, Chunshi Jilin Univ Sch Mech Sci & Engn Changchun Jilin Peoples R China Shenyang Machine Tool Co Ltd Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538633229

The reliability of the product is getting higher and higher. Because of product's performance will be degraded as it is used, so the degradation analysis becomes an important method to evaluate the reliability of the product. In this paper, the stochastic process is used to describe the degradation process of the product. For describing the difference between the products, the random variable is introduced in the Inverse Gaussian model. But there is the latent variable in this model. The algorithm is used to solve parameters in this model. The correctness of this method is verified by simulation. Finally, this method is applied to the degraded data of heavy machine tool.

关键词： degradation inverse Gaussian stochastic process em algorithm heavy machine tool

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FAST algorithm FOR STATISTICAL PHRASE/ACCENT COMMAND ESTIMATION BASED ON GENERATIVE MODEL INCORPORATING SPECTRAL FEATURES

FAST ALGORITHM FOR STATISTICAL PHRASE/ACCENT COMMAND ESTIMAT...

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

作者： Sato, Ryotaro Kameoka, Hirokazu Kashino, Kunio Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan NTT Corp NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9781509041176

An important challenge in speech processing involves extracting non-linguistic information from a fundamental frequency (F-0) contour of speech. We propose a fast algorithm for estimating the model parameters of the Fujisaki model, namely, the timings and magnitudes of the phrase and accent commands. Although a powerful parameter estimation framework based on a stochastic counterpart of the Fujisaki model has recently been proposed, it still had room for improvement in terms of both computational efficiency and parameter estimation accuracy. This paper describes our two contributions. First, we propose a hard expectation-maximization (em) algorithm for parameter inference where the E step of the conventional em algorithm is replaced with a point estimation procedure to accelerate the estimation process. Second, to improve the parameter estimation accuracy, we add a generative process of a spectral feature sequence to the generative model. This makes it possible to use linguistic or phonological information as an additional clue to estimate the timings of the accent commands. The experiments confirmed that the present algorithm was approximately 16 times faster and estimated parameters about 3% more accurately than the conventional algorithm.

关键词： voice fundamental frequency contour Fujisaki model prosodic information processing em algorithm

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A Robust Inference algorithm for Crowd Sourced Categorization 12

A Robust Inference Algorithm for Crowd Sourced Categorizatio...

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12th International Conference on Intelligent Systems and Knowledge Engineering (IEEE ISKE)

作者： Wu, Ming Li, Qianmu Zhang, Jing Cui, Shicheng Li, Deqiang Qi, Yong Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9781538618295

With the rapid growing of crowdsourcing systems, class labels for supervised learning can be easily obtained from crowdsourcing platforms. To deal with the problem that labels obtained from crowds are usually noisy due to imperfect reliability of non-expert workers, we let multiple workers provide labels for the same object. Then, true labels of the labeled object are estimated through ground truth inference algorithms. The inferred integrated labels are expected to be of high quality. In this paper, we propose a novel ground truth inference algorithm based on em algorithm, which not only infers the true labels of the instances but also simultaneously estimates the reliability of each worker and the difficulty of each instance. Experimental results on seven real-world crowdsourcing datasets show that our proposed algorithm outperforms eight state-of-the art algorithms.

关键词： crowdsourcing ground truth inference em algorithm label integration

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Attenuation Correction in SPECT Images Using Attenuation Map Estimation with Its emission Data

Attenuation Correction in SPECT Images Using Attenuation Map...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Tavakoli, Meysam Naji, Maryam Abdollahi, Ali Kalantari, Faraz Indiana Univ Purdue Univ Dept Phys Indianapolis IN 46205 USA Imam Reza Hosp Mashhad Iran Oklahoma State Univ Dept Mech Engn Stillwater OK 74078 USA Univ Texas Southwestern Dept Radiat Oncol Dallas TX USA

ISBN: (数字)9781510607101

ISBN: (纸本)9781510607095;9781510607101

Photon attenuation during SPECT imaging significantly degrades the diagnostic outcome and the quantitative accuracy of final reconstructed images. It is well known that attenuation correction can be done by using iterative reconstruction methods if we access to attenuation map. Two methods have been used to calculate the attenuation map: transmission- based and transmissionless techniques. In this phantom study, we evaluated the importance of attenuation correction by quantitative evaluation of errors associated with each method. For transmissionless approach, the attenuation map was estimated from the emission data only. An em algorithm with attenuation model was developed and used for attenuation correction during image reconstruction. Finally, a comparison was done between reconstructed images using our OSem code and analytical FBP method before and after attenuation correction. The results of measurements showed that: our programs are capable to reconstruct SPECT images and correct the attenuation effects. Moreover, to evaluate reconstructed image quality before and after attenuation correction we applied a novel approach using Image Quality Index. Attenuation correction increases the quality and quantitative accuracy in both methods. This increase is independent of activity in quantity factor and decreases with activity in quality factor. In em algorithm, it is necessary to use regularization to obtain true distribution of attenuation coefficients.

关键词： Attenuation correction OSem image reconstruction SPECT em algorithm attenuation map image quality index

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Examining the effect of initialization strategies on the performance of Gaussian mixture modeling

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BEHAVIOR RESEARCH METHODS 2017年第1期49卷 282-293页

作者： Shireman, emilie Steinley, Douglas Brusco, Michael J. Univ Missouri Columbia MO 65211 USA Florida State Univ Tallahassee FL 32306 USA

Mixture modeling is a popular technique for identifying unobserved subpopulations (e.g., components) within a data set, with Gaussian (normal) mixture modeling being the form most widely used. Generally, the parameters of these Gaussian mixtures cannot be estimated in closed form, so estimates are typically obtained via an iterative process. The most common estimation procedure is maximum likelihood via the expectation-maximization (em) algorithm. Like many approaches for identifying subpopulations, finite mixture modeling can suffer from locally optimal solutions, and the final parameter estimates are dependent on the initial starting values of the em algorithm. Initial values have been shown to significantly impact the quality of the solution, and researchers have proposed several approaches for selecting the set of starting values. Five techniques for obtaining starting values that are implemented in popular software packages are compared. Their performances are assessed in terms of the following four measures: (1) the ability to find the best observed solution, (2) settling on a solution that classifies observations correctly, (3) the number of local solutions found by each technique, and (4) the speed at which the start values are obtained. On the basis of these results, a set of recommendations is provided to the user.

关键词： Mixture modeling em algorithm algorithm initialization

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A GEOMETRIC LEARNING APPROACH ON THE SPACE OF COMPLEX COVARIANCE MATRICES

A GEOMETRIC LEARNING APPROACH ON THE SPACE OF COMPLEX COVARI...

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

作者： Hajri, Hatem Said, Salem Bombrun, Lionel Berthoumieu, Yannick Univ Bordeaux Lab IMS CNRS UMR 5218 Bordeaux France

ISBN: (纸本)9781509041176

Many signal and image processing applications, including SAR polarimetry and texture analysis, require the classification of complex covariance matrices. The present paper introduces a geometric learning approach on the space of complex covariance matrices based on a new distribution called Riemannian Gaussian distribution. The proposed distribution has two parameters, the centre of mass (Y) over bar Y and the dispersion parameter sigma. After having derived its maximum likelihood estimator and its extension to mixture models, we propose an application to texture recognition on the VisTex database.

关键词： Classification Hermitian matrices Information geometry Riemannian centre of mass em algorithm

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Shape-based Trajectory Clustering 6

Shape-based Trajectory Clustering

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6th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Pires, Telmo J. P. Figueiredo, Mario A. T. Univ Lisbon Inst Telecomunicacoes Lisbon Portugal Univ Lisbon Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9789897582226

Automatic trajectory classification has countless applications, ranging from the natural sciences, such as zoology and meteorology, to urban planning, sports analysis, and surveillance, and has generated great research interest. This paper proposes and evaluates three new methods for trajectory clustering, strictly based on the trajectory shapes, thus invariant under changes in spatial position and scale (and, optionally, orientation). To extract shape information, the trajectories are first uniformly resampled using splines, and then described by the sequence of tangent angles at the resampled points. Dealing with angular data is challenging, namely due to its periodic nature, which needs to be taken into account when designing any clustering technique. In this context, we propose three methods: a variant of the k-means algorithm, based on a dissimilarity measure that is adequate for angular data;a finite mixture of multivariate Von Mises distributions, which is fitted using an em algorithm;sparse nonnegative matrix factorization, using complex representation of the angular data. Methods for the automatic selection of the number of clusters are also introduced. Finally, these techniques are tested and compared on both real and synthetic data, demonstrating their viability.

关键词： Unsupervised Learning Directional Statistics k-means em algorithm Nonnegative Matrix Factorization Trajectory Clustering

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