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A finite mixture model of vehicle-to-vehicle and day-to-day variability of traffic network travel times

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2014年 46卷 83-97页

作者： Kim, Jiwon Mahmassani, Hani S. Northeastern Univ Transportat Ctr Evanston IL 60208 USA

This study proposes an approach to modeling the effects of daily roadway conditions on travel time variability using a finite mixture model based on the Gamma-Gamma (GG) distribution. The GG distribution is a compound distribution derived from the product of two Gamma random variates, which represent vehicle-to-vehicle and day-to-day variability, respectively. It provides a systematic way of investigating different variability dimensions reflected in travel time data. To identify the underlying distribution of each type of variability, this study first decomposes a mixture of Gamma-Gamma models into two separate Gamma mixture modeling problems and estimates the respective parameters using the expectation-maximization (EM) algorithm. The proposed methodology is demonstrated using simulated vehicle trajectories produced under daily scenarios constructed from historical weather and accident data. The parameter estimation results suggest that day-today variability exhibits clear heterogeneity under different weather conditions: clear versus rainy or snowy days, whereas the same weather conditions have little impact on vehicle-to-vehicle variability. Next, a two-component Gamma-Gamma mixture model is specified. The results of the distribution fitting show that the mixture model provides better fits to travel delay observations than the standard (one-component) Gamma-Gamma model. The proposed method, the application of the compound Gamma distribution combined with a mixture modeling approach, provides a powerful and flexible tool to capture not only different types of variability vehicle-to-vehicle and day-to-day variability but also the unobserved heterogeneity within these variability types, thereby allowing the modeling of the underlying distributions of individual travel delays across different days with varying roadway disruption levels in a more effective and systematic way. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Travel time reliability Travel time variability Finite mixture model Gamma-Gamma distribution expectation-maximization algorithm

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Distributed Sparse Recursive Least-Squares Over Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第6期62卷 1386-1395页

作者： Liu, Zhaoting Liu, Ying Li, Chunguang Shaoxing Univ Dept Elect Engn Shaoxing 312000 Peoples R China Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

Distributed estimation over networks has received much attention in recent years due to its broad applicability. Many signals in nature present high level of sparsity, which contain only a few large coefficients among many negligible ones. In this paper, we address the problem of in-network distributed estimation for sparse vectors, and develop several distributed sparse recursive least-squares (RLS) algorithms. The proposed algorithms are based on the maximum likelihood framework, and the expectation-maximization algorithm, with the aid of thresholding operators, is used to numerically solve the sparse estimation problem. To improve the estimation performance, the thresholding operators related to l(0)- and l(1)-norms with real-time self-adjustable thresholds are derived. With these thresholding operators, we can exploit the underlying sparsity to implement the distributed estimation with low computational complexity and information exchange amount among neighbors. The sparsity- promoting intensity is also adaptively adjusted so that a good performance of the sparse solution can be achieved. Both theoretical analysis and numerical simulations are presented to show the effectiveness of the proposed algorithms.

关键词： Wireless sensor network distributed estimation sparsity adaptive signal processing recursive least square expectation-maximization algorithm

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Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2014年 69卷 173-183页

作者： Fu, Liyong Wang, Mingliang Lei, Yuancai Tang, Shouzheng Chinese Acad Forestry Res Inst Forest Resource Informat Tech Beijing 100091 Peoples R China Univ Georgia Warnell Sch Forestry & Nat Resources Athens GA 30602 USA

Multi-level nonlinear mixed effects (ML-NLME) models have received a great deal of attention in recent years because of the flexibility they offer in handling the repeated-measures data arising from various disciplines. In this study, we propose both maximum likelihood and restricted maximum likelihood estimations of ML-NLME models with two-level random effects, using first order conditional expansion (FOCE) and the expectation-maximization (EM) algorithm. The FOCE EM algorithm was compared with the most popular Lindstrom and Bates (LB) method in terms of computational and statistical properties. Basal area growth series data measured from Chinese fir (Cunninghamia lanceolata) experimental stands and simulated data were used for evaluation. The FOCE EM and LB algorithms given the same parameter estimates and fit statistics for models that converged by both. However, FOCE EM converged for all the models, while LB did not, especially for the models in which two-level random effects are simultaneously considered in several base parameters to account for between-group variation. We recommend the use of FOCE EM in ML-NLME models, particularly when convergence is a concern in model selection. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Cunninghamia lanceolata expectation-maximization algorithm First order conditional expansion Lindstrom and Bates algorithm Simulated data Two-level nonlinear mixed effects models

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Reducing the uncertainties in the achievable vehicle performance targets through design optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2014年第9期228卷 1061-1083页

作者： Yuen, Tey Jing Ramli, Rahizar Univ Malaya Fac Engn Dept Mech Engn Kuala Lumpur 50603 Malaysia

In this paper an alternative method is proposed for defining the suspension performance targets through the use of full-vehicle modelling consisting of a ride model and a handling model. These models are derived with the use of a non-linear damper, suspension kinematic characteristics and basic vehicle dimensions. The vehicle performances can be explored using the design-of-experiments method. The non-sorting method is then employed to sort for non-dominated solutions, where these samples represent the Pareto front of the vehicle performances in ride comfort and handling. The k-means clustering method is used to classify further the solution into different unique optimum characteristics. The expectation-maximization algorithm is developed to compute the allowable variance of design parameters required to achieve the specific optimum design targets. This method can be a very useful tool in the earliest design stages where vehicle data are inadequate. This methodology potentially reduces the uncertainty in the achievable vehicle performance targets by allowing engineers to compare the optimum limit of the suspension with those of benchmark vehicles in the early suspension design and development process

关键词： Ride model handling model k-means clustering non-sorting method design-of-experiments method expectation-maximization algorithm suspension design

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Mixtures of equispaced normal distributions and their use for testing symmetry with univariate data

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2014年 71卷 262-272页

作者： Bacci, Silvia Bartolucci, Francesco Univ Perugia IT Dept Econ Finance & Stat Perugia Italy

Given a random sample of observations, mixtures of normal densities are often used to estimate the unknown continuous distribution from which the data come. The use of this semi-parametric framework is proposed for testing symmetry about an unknown value. More precisely, it is shown how the null hypothesis of symmetry may be formulated in terms of a normal mixture model, with weights about the center of symmetry constrained to be equal one another. The resulting model is nested in a more general unconstrained one, with the same number of mixture components and free weights. Therefore, after having maximized the constrained and unconstrained log-likelihoods, by means of the expectation-maximization algorithm, symmetry is tested against skewness through a likelihood ratio statistic with p-value computed by using a parametric bootstrap method. The behavior of this mixture-based test is studied through a Monte Carlo simulation, where the proposed test is compared with the traditional one, based on the third standardized moment, and with the non-parametric triples test. An illustrative example is also given which is based on real data. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Bootstrap method Density estimation expectation-maximization algorithm Skewness

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Automatic detection of wakefulness and rest intervals in actigraphic signals: A data-driven approach

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MEDICAL ENGINEERING & PHYSICS 2014年第12期36卷 1585-1592页

作者： Martin-Martinez, D. Casaseca-de-la-Higuera, P. Andres-de-Llano, J. M. Garmendia-Leiza, J. R. Alberola-Lopez, S. Alberola-Lopez, C. Univ Valladolid Lab Proc Imagen Valladolid Spain Univ West Scotland Audio Visual Commun & Networking Res Ctr Paisley Renfrew Scotland

Actigraphy is an useful tool for evaluating the activity pattern of a subject;activity registries are usually processed by first splitting the signal into its wakefulness and rest intervals and then analyzing each one in isolation. Consequently, a preprocessing stage for such a splitting is needed. Several methods have been reported to this end but they rely on parameters and thresholds which are manually set based on previous knowledge of the signals or learned from training. This compromises the general applicability of this methods. In this paper we propose a new method in which thresholds are automatically set based solely on the specific registry to be analyzed. The method consists of two stages: (1) estimation of an initial classification mask by means of the expectation maximization algorithm and (2) estimation of a final refined mask through an iterative method which re-estimates both the mask and the classifier parameters at each iteration step. Results on real data show that our methodology outperforms those so far proposed and can be more effectively used to obtain derived sleep quality parameters from actigraphy registries. (C) 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

关键词： Actigraphy Wakefulness/Rest detection Probability density function expectation-maximization algorithm

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Trajectory Modeling of Longitudinal Binary Data: Application of the EM algorithm for Mixture Models

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2014年第3期43卷 495-519页

作者： Chu, Man-Kee M. Koval, John J. Univ Western Ontario Dept Stat & Actuarial Sci London ON N6A 5B7 Canada Univ Western Ontario Dept Epidemiol & Biostat London ON N6A 5B7 Canada

A developmental trajectory describes the course of behavior over time. Identifying multiple trajectories within an overall developmental process permits a focus on subgroups of particular interest. We introduce a framework for identifying trajectories by using the expectation-maximization (EM) algorithm to fit semiparametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algorithms (PROC TRAJ in SAS), standard EM, and two other EM-based algorithms for speeding up convergence. Simulation shows that EM methods produce more accurate parameter estimates. The EM methodology is illustrated with a longitudinal dataset involving adolescents smoking behaviors.

关键词： Binary data expectation-maximization algorithm Longitudinal trajectories Mixture models

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Trends and variability of seasonal weather regimes

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INTERNATIONAL JOURNAL OF CLIMATOLOGY 2014年第2期34卷 472-480页

作者： Vrac, Mathieu Ayar, Pradeebane Vaittinada Yiou, Pascal CNRS CEA UVSQ Ctr Etud Saclay Lab Sci Climat & Environm LSCE IPSL F-91191 Gif Sur Yvette France

Seasons and seasonality are the main properties of extra-tropical climate that affect ecosystems and society. For example, agriculture, tourism, energy consumption or ecosystem phenology are primarily dependent on seasonality and on the magnitude of the meteorological events associated within each season. Changes in the seasonality of variables like surface temperature during the last decades have been widely investigated but seasonal changes of the weather have received less quantification. This paper redefines the concept of seasonality based on the extra-tropical atmospheric circulation, and on the notion that it can drive the evolution of temperature. We find that summer-like atmospheric conditions have appeared earlier and ended later since 1948. Conversely, the period with winter patterns has reduced over that period. The temperatures associated with weather patterns allow to identify the sources of temperature trends.

关键词： clustering expectation-maximization algorithm mixture model

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Calibrating the exponential Ornstein-Uhlenbeck multiscale stochastic volatility model

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QUANTITATIVE FINANCE 2014年第3期14卷 443-456页

作者： Dubarry, Cyrille Douc, Randal Telecom SudParis CITI Evry France

This paper demonstrates a tractable and efficient way of calibrating a multiscale exponential Ornstein-Uhlenbeck stochastic volatility model including a correlation between the asset return and its volatility. As opposed to many contributions where this correlation is assumed to be null, this framework allows one to describe the leverage effect widely observed in equity markets. The resulting model is non-exponential and driven by a degenerate noise, thus requiring a high level of care in designing the estimation algorithm. The way this difficulty is overcome provides guidelines concerning the development of an estimation algorithm in a non-standard framework. The authors propose using a block-type expectation maximization algorithm along with particle smoothing. This method results in an accurate calibration process able to identify up to three timescale factors. Furthermore, we introduce an intuitive heuristic which can be used to choose the number of factors.

关键词： Multiscale stochastic volatility model Inference Particle smoothing Maximum split data estimate expectation-maximization algorithm

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Longitudinal analysis of self-reported health status by mixture latent auto-regressive models

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2014年第2期63卷 267-288页

作者： Bartolucci, Francesco Bacci, Silvia Pennoni, Fulvia Univ Perugia I-06123 Perugia Italy Univ Milano Bicocca Milan Italy

Motivated by an application to a longitudinal data set coming from the Health and Retirement Study about self-reported health status, we propose a model for longitudinal data which is based on a latent process to account for the unobserved heterogeneity between sample units in a dynamic fashion. The latent process is modelled by a mixture of auto-regressive AR(1) processes with different means and correlation coefficients, but with equal variances. We show how to perform maximum likelihood estimation of the proposed model by the joint use of an expectation-maximization algorithm and a Newton-Raphson algorithm, implemented by means of recursions developed in the hidden Markov model literature. We also introduce a simple method to obtain standard errors for the parameter estimates and suggest a strategy to choose the number of mixture components. In the application the response variable is ordinal;however, the approach may also be applied in other settings. Moreover, the application to the self-reported health status data set allows us to show that the model proposed is more flexible than other models for longitudinal data based on a continuous latent process. The model also achieves a goodness of fit that is similar to that of models based on a discrete latent process following a Markov chain, while retaining a reduced number of parameters. The effect of different formulations of the latent structure of the model is evaluated in terms of estimates of the regression parameters for the covariates.

关键词： Quadrature methods Latent Markov model Hidden Markov model expectation-maximization algorithm Proportional odds model

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