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Finite mixtures of canonical fundamental skew t-distributions The unification of the restricted and unrestricted skew t-mixture models

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STATISTICS AND COMPUTING 2016年第3期26卷 573-589页

作者： Lee, Sharon X. McLachlan, Geoffrey J. Univ Queensland Dept Math St Lucia Qld 4072 Australia

This paper introduces a finite mixture of canonical fundamental skew t (CFUST) distributions for a model-based approach to clustering where the clusters are asymmetric and possibly long-tailed (in: Lee and McLachlan, arXiv: 1401.8182 [statME], 2014b). The family of CFUST distributions includes the restricted multivariate skew t and unrestricted multivariate skew t distributions as special cases. In recent years, a few versions of the multivariate skew t (MST) mixture model have been put forward, together with various em-type algorithms for parameter estimation. These formulations adopted either a restricted or unrestricted characterization for their MST densities. In this paper, we examine a natural generalization of these developments, employing the CFUST distribution as the parametric family for the component distributions, and point out that the restricted and unrestricted characterizations can be unified under this general formulation. We show that an exact implementation of the em algorithm can be achieved for the CFUST distribution and mixtures of this distribution, and present some new analytical results for a conditional expectation involved in the E-step.

关键词： Mixture models em algorithm Skew normal distributions Skew t distributions Fundamental skew distributions

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Traffic matrix estimation: A neural network approach with extended input and expectation maximization iteration

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2016年 60卷 220-232页

作者： Zhou, Haifeng Tan, Liansheng Zeng, Qian Wu, Chunming Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Cent China Normal Univ Dept Comp Sci Wuhan 430079 Peoples R China Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China

Accurately estimating of IP Traffic matrix (TM) is still a challenging task and it has wide applications in network management, load-balancing, traffic detecting and so on. In this paper, we propose an accurate method, i.e., the Moore-Penrose inverse based neural network approach for the estimation of IP network traffic matrix with extended input and expectation maximization iteration, which is termed as MNETME for short. Firstly, MNETME adopts the extended input component, i.e., the product of routing matrix's Moore-Penrose inverse and the link load vector, as the input to the neural network. Secondly, the em algorithm is incorporated into its architecture to deal with the output data of the neural network. Therefore, MNETME manifests itself with the advantages that-it needs less input data, but has better accuracy of estimation. We theoretically analyze the algorithm and then study its performance using the real data from the Abilene Network. The simulation results show that MNETME leads to a more accurate estimation in contrast to the previous methods, meanwhile it holds better robustness and can well track the traffic fluctuations. We finally extend MNETME to random routing networks by proposing a new model of random routing which overcomes three fatal deficiencies of the existing model and it is easier, more practical and more precise. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Traffic matrix Neural network em algorithm Moore-Penrose inverse Singular value decomposition (SVD) IP network

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A Bayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data

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AUTOMATION IN CONSTRUCTION 2016年 72卷 237-246页

作者： Zhan, Xianyuan Ukkusuri, Satish V. Yang, Chao Purdue Univ Lyles Sch Civil Engn 550 Stadium Mall Dr W Lafayette IN 47907 USA Tongji Univ Sch Transportat Engn Minist Educ Key Lab Rd & Traff Engn 4800 Caoan Rd Shanghai 201804 Peoples R China

Accurate estimation and prediction of urban link travel times are important for urban traffic operations and management. This paper develops a Bayesian mixture model to estimate short-term average urban link travel times using large-scale trip-based data with partial information. Unlike typical GPS trajectory data, trip-based data from taxies or other sources provide limited trip level information, which only contains the trip origin and destination locations, trip travel times and distances, etc. The focus of this study is to develop a robust probabilistic short-term average link travel time estimation model and demonstrate the feasibility of estimating network conditions using large-scale trip level information. In the model, the path taken by each trip is considered as latent and modeled using a multinomial logit distribution. The observed trip data given the possible path set and the mean and variance of the average link travel times can thus be characterized using a finite mixture distribution. A transition model is also introduced to serve as an informative prior that captures the temporal and spatial dependencies of link travel times. A solution approach based on the expectation-maximization (em) algorithm is proposed to solve the problem. The model is tested on estimating the mean and variance of the average link travel times for 30 min time intervals using a large-scale taxi trip dataset from New York City. More robust estimation results are obtained owing to the adoption of the Bayesian framework. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Short-term average link travel time estimation Trip based data with partial information Bayesian mixture model Path inference em algorithm

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A variational Expectation-Maximization algorithm for temporal data clustering

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2016年 103卷 206-228页

作者： El Assaad, Hani Same, Allou Govaert, Gerard Aknin, Patrice Univ Paris Est IFSTTAR GRETTIA F-77447 Champs Sur Marne France Univ Technol Compiegne UMR CNRS Heudiasyc 7253 F-60205 Compiegne France SNCF Res & Innovat F-75012 Paris France

The problem of temporal data clustering is addressed using a dynamic Gaussian mixture model. In addition to the missing clusters used in the classical Gaussian mixture model, the proposed approach assumes that the means of the Gaussian densities are latent variables distributed according to random walks. The parameters of the proposed algorithm are estimated by the maximum likelihood approach. However, the em algorithm cannot be applied directly due to the complex structure of the model, and some approximations are required. Using a variational approximation, an algorithm called Vem-DyMix is proposed to estimate the parameters of the proposed model. Using simulated data, the ability of the proposed approach to accurately estimate the parameters is demonstrated. Vem-DyMix outperforms, in terms of clustering and estimation accuracy, other state-of-the-art algorithms. The experiments performed on real world data from two fields of application (railway condition monitoring and object tracking from videos) show the strong potential of the proposed algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Temporal data clustering Dynamic latent variable model Mixture model em algorithm Kalman filter Clustering Maximum likelihood Variational approximation

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A multilevel finite mixture item response model to cluster examinees and schools

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2016年第1期10卷 53-70页

作者： Gnaldi, Michela Bacci, Silvia Bartolucci, Francesco Univ Perugia Dept Polit Sci Via A Pascoli 20 I-06123 Perugia Italy Univ Perugia Dept Econ Via A Pascoli 20 I-06123 Perugia Italy

Within the educational context, a key goal is to assess students' acquired skills and to cluster students according to their ability level. In this regard, a relevant element to be accounted for is the possible effect of the school students come from. For this aim, we provide a methodological tool which takes into account the multilevel structure of the data (i.e., students in schools) and allows us to cluster both students and schools into homogeneous classes of ability and effectiveness, and to assess the effect of certain students' and school characteristics on the probability to belong to such classes. The proposed approach relies on an extended class of multidimensional latent class IRT models characterised by: (i) latent traits defined at student and school level, (ii) latent traits represented through random vectors with a discrete distribution, (iii) the inclusion of covariates at student and school level, and (iv) a two-parameter logistic parametrisation for the conditional probability of a correct response given the ability. The approach is applied for the analysis of data collected by two national tests administered in Italy to middle school students in June 2009: the INVALSI Language Test and the Mathematics Test.

关键词： em algorithm INVALSI Tests Latent class model Multilevel multidimensional item response models Two-parameter logistic model

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Testing hypothesis for a simple ordering in incomplete contingency tables

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2016年 99卷 25-37页

作者： Li, Hui-Qiong Tian, Guo-Liang Jiang, Xue-Jun Tang, Nian-Sheng Yunnan Univ Dept Stat Kunming 650091 Yunnan Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Pokfulam Rd Hong Kong Hong Kong Peoples R China South Univ Sci & Technol China Dept Math Shenzhen 518055 Guangdong Peoples R China

A test for ordered categorical variables is of considerable importance, because they are frequently encountered in biomedical studies. This paper introduces a simple ordering test approach for the two-way r x c contingency tables with incomplete counts by developing six test statistics, i.e., the likelihood ratio test statistic, score test statistic, global score test statistic, Hausman-Wald test statistic,Wald test statistic and distance-based test statistic. Bootstrap resampling methods are also presented. The performance of the proposed tests is evaluated with respect to their empirical type I error rates and empirical powers. The results show that the likelihood ratio test statistic based on the bootstrap resampling methods perform satisfactorily for small to large sample sizes. A real example from a wheeze study in six cities is used to illustrate the proposed methodologies. (C) 2016 Elsevier B.V. All rights reserved.

关键词： em algorithm Incomplete contingency tables Ordering Statistical inferences

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Hybrid Maximum Likelihood Modulation Classification for Continuous Phase Modulations

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IEEE COMMUNICATIONS LETTERS 2016年第3期20卷 450-453页

作者： Yuan, Yabo Zhao, Peng Wang, Bo Wu, Bin Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China Key Lab Space Object Measurement Beijing 100094 Peoples R China

In this letter, we propose a hybrid maximum likelihood (HML) classifier for continuous phase modulation (CPM). To the best of our knowledge, the proposed likelihood function is the first one for CPM signals that is based on two of its main features: nonlinear waveform, which is represented with its principal components, and signal memory, which is modeled as a Markov mapping symbol sequence. Unknown channel parameters are estimated through the expectation-maximization (em) algorithm. An approximation method is further proposed to ensure that the proposed classifier improves classification performance at the cost of a moderate increase in calculations. Numerical results prove the superiority of the proposed approach over the classical HML classifier and feature-based classifier in terms of classifying CPM and linear modulation.

关键词： Automatic modulation classification CPM ML estimation em algorithm principal component analysis

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Mixture models for ordinal data: a pairwise likelihood approach

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STATISTICS AND COMPUTING 2016年第1-2期26卷 529-547页

作者： Ranalli, Monia Rocci, Roberto Univ Roma La Sapienza Dept Stat Piazzale Aldo Moro 5 I-00185 Rome Italy Univ Roma Tor Vergata IGF Dept Via Columbia 2 I-00133 Rome Italy

Alatent Gaussian mixture model to classify ordinal data is proposed. The observed categorical variables are considered as a discretization of an underlying finite mixture of Gaussians. The model is estimated within the expectation-maximization (em) framework maximizing a pairwise likelihood. This allows us to overcome the computational problems arising in the full maximum likelihood approach due to the evaluation of multidimensional integrals that cannot be written in closed form. Moreover, a method to cluster the observations on the basis of the posterior probabilities in output of the pairwise em algorithm is suggested. The effectiveness of the proposal is shown comparing the pairwise likelihood approach with the full maximum likelihood and the maximum likelihood for continuous data ignoring the ordinal nature of the variables. The comparison is made by means of a simulation study;applications to real data are provided.

关键词： Finite mixture models Composite likelihood em algorithm Ordinal data

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Simultaneous variable selection and de-coarsening in multi-path change-point models

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JOURNAL OF MULTIVARIATE ANALYSIS 2016年 147卷 202-217页

作者： Shohoudi, Azadeh Khalili, Abbas Wolfson, David B. Asgharian, Masoud McGill Univ Dept Math & Stat Montreal PQ H3A 0B9 Canada

Follow-up studies on a group of units are commonly carried out to explore the possibility that a response distribution has changed at unobservable time points that are different for different units. Often, in practice, there will be many potential covariates, which may not only be associated with the response distribution but also with the distribution of the unobservable change-points. Here, the covariates are allowed to enter the change point distribution through a proportional odds model whose baseline odds is assumed to be piecewise constant as a function of time. The combination of a large number of putative regression coefficients in the response distributions as well as the change-point distribution, alone leads to a challenging simultaneous variable selection and estimation problem. Moreover, selection and estimation of the parameters that determine the coarseness of the baseline odds function adds a further level of complexity. Using penalized likelihood methods we are able to simultaneously perform variable selection, estimation, and determine the coarseness of the baseline odds function. Our approach is computationally efficient and shown to be consistent in variable selection and parameter estimation. We assess its performance through simulations, and demonstrate its usage in fitting a model for cognitive decline in subjects with Alzheimer's disease. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Change-point models em algorithm Regularization LASSO SCAD Alzheimer's disease

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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

作者： Ryotaro Sato Hirokazu Kameoka Kunio Kashino Graduate School of Information Science and Technology The University of Tokyo Japan NTT Communication Science Laboratories NTT Corporation Japan

ISBN: (纸本)9781509041183

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 expectation-maximisation algorithm Parameter estimation spectral feature commands Sodium Glutamate Speech processing model parameters Linguistics

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