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On the selection of samples for multivariate regression analysis: application to near-infrared (NIR) calibration models for the prediction of pulp yield in Eucalyptus nitens

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CANADIAN JOURNAL OF FOREST RESEARCH 2008年第10期38卷 2626-2634页

作者： Mora, Christian R. Schimleck, Laurence R. Univ Georgia Warnell Sch Forestry & Nat Resources Athens GA 30602 USA Univ Georgia Warnell Sch Forestry & Nat Resources Athens GA 30602 USA

The effects of using reduced calibration sets on the development of near-infrared (NIR) calibration models for the prediction of kraft pulp yield in Eucalyptus nitens (Dean & Maiden) Maiden trees were explored. Three selection techniques based on NIR spectral data (CADEX (computer-aided design of experiments), DUPLEX, and SELECT algorithms) and one selection method based on a measured property (RANKING algorithm) were used for analysis and compared against a model using all data. The effect of using calibration sets of different sizes was also evaluated. All sample-selection methods resulted in models of similar performance compared with the model fitted using all samples. For calibration purposes, RANKING selection resulted in models with the lowest errors of cross-validation, followed by the DUPLEX, CADEX, and SELECT methods. In terms of validation, the SELECT and CADEX methods resulted in lower errors of prediction compared with the DUPLEX and RANKING algorithms. In general, cross-validation and prediction errors decreased as the number of calibration samples increased. These results show that it is possible to obtain adequate NIR calibration models with a reduced number of samples allowing the remaining samples to be used for model validation and that sample selection based on NIR spectral data alone is as successful as selection based on a measured property.

关键词： algorithms READING COMPUTER-aided design COMPUTER-aided engineering DESIGN ALGEBRA ARITHMETIC -- Foundations COMPUTER programming expectation-maximization algorithms

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Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2013年第2期61卷 340-354页

作者： Ziniel, Justin Schniter, Philip Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

In this work, a Bayesian approximate message passing algorithm is proposed for solving the multiple measurement vector (MMV) problem in compressive sensing, in which a collection of sparse signal vectors that share a common support are recovered from undersampled noisy measurements. The algorithm, AMP-MMV, is capable of exploiting temporal correlations in the amplitudes of non-zero coefficients, and provides soft estimates of the signal vectors as well as the underlying support. Central to the proposed approach is an extension of recently developed approximate message passing techniques to the amplitude-correlated MMV setting. Aided by these techniques, AMP-MMV offers a computational complexity that is linear in all problem dimensions. In order to allow for automatic parameter tuning, an expectation-maximization algorithm that complements AMP-MMV is described. Finally, a detailed numerical study demonstrates the power of the proposed approach and its particular suitability for application to high-dimensional problems.

关键词： Approximate message passing (AMP) belief propagation compressed sensing expectation-maximization algorithms joint sparsity Kalman filters multiple measurement vector problem statistical signal processing

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CORRECTIONS FOR ACCIDENTAL COINCIDENCES AND ATTENUATION IN MAXIMUM-LIKELIHOOD IMAGE-RECONSTRUCTION FOR POSITRON-EMISSION TOMOGRAPHY

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1991年第1期10卷 82-89页

作者： POLITTE, DG SNYDER, DL Washington University Saint Louis MO USA

A new method has been developed and tested for correcting for the effects of accidental coincidences and photon attenuation in positron-emission tomography. A mathematical model is used for these effects as the basis for the development. The corrections are incorporated into the iterations of an expectation-maximization algorithm for numerically producing the maximum-likelihood estimate of a distribution of radioactivity within a patient. We have found the images reconstructed with this new procedure to be unbiased and to exhibit lower variance than those reconstructed from precorrected data.

关键词： Attenuation Image reconstruction Maximum likelihood estimation Maximum likelihood detection expectation-maximization algorithms Tomography Image recognition Optical attenuators Photonic crystals Degradation

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DWT-CEM: an algorithm for scale-temporal clustering in fMRI

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BIOLOGICAL CYBERNETICS 2007年第1期97卷 33-45页

作者： Sato, Joao Ricardo Fujita, Andre Amaro, Edson, Jr. Miranda, Janaina Mourao Morettin, Pedro Alberto Brammer, Michal John Univ Sao Paulo Inst Math & Stat BR-05508090 Sao Paulo Brazil Univ Sao Paulo Dept Radiobiochem LIM44 NIF BR-05403001 Sao Paulo Brazil Kings Coll London Inst Psychiat Brain Image Anal Unit London SE5 8AF England

The number of studies using functional magnetic resonance imaging (fMRI) has grown very rapidly since the first description of the technique in the early 1990s. Most published studies have utilized data analysis methods based on voxel-wise application of general linear models (GLM). On the other hand, temporal clustering analysis (TCA) focuses on the identification of relationships between cortical areas by measuring temporal common properties. In its most general form, TCA is sensitive to the low signal-to-noise ratio of BOLD and is dependent on subjective choices of filtering parameters. In this paper, we introduce a method for wavelet-based clustering of time-series data and show that it may be useful in data sets with low signal-to-noise ratios, allowing the automatic selection of the optimum number of clusters. We also provide examples of the technique applied to simulated and real fMRI datasets.

关键词： CLUSTER analysis (Statistics) MAGNETIC resonance imaging SIGNAL-to-noise ratio expectation-maximization algorithms GAUSSIAN distribution WAVELETS (Mathematics) LINEAR models (Statistics)

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Novel Bayesian Vectorcardiographic Loop Alignment for Improved Monitoring of ECG and Fetal Movement

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2013年第6期60卷 1580-1588页

作者： Vullings, Rik Mischi, Massimo Oei, S. Guid Bergmans, Jan W. M. Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands Maxima Med Ctr Dept Obstet & Gynecol NL-5500 MB Veldhoven Netherlands

The continuous analysis of electrocardiographic (ECG) signals is complicated by morphological variability in the ECG due to movement of the heart. By aligning vectorcardiographic loops, movement-induced ECG variations can be partly corrected for. Existing methods for loop alignment can account for loop rotation, scaling, and time delays, but they lack the possibility to include a priori information on any of these transformations, and they are unreliable in case of low-quality signals, such as fetal ECG signals. The inclusion of a priori information might aid in the robustness of loop alignment and is, hence, proposed in this paper. We provide a generic Bayesian framework to derive our loop alignment method. In this framework, existing methods can be readily derived as well, as a simplification of our method. The loop alignment is evaluated by comparing its performance in loop alignment to two existing methods, for both adult and fetal ECG recordings. For the adult ECG recordings, a quantitative performance assessment shows that the developed method outperforms the existing method in terms of robustness. For the fetal ECG recordings, it is demonstrated that the developed method can be used to correct ECG signals for movement-induced morphology changes (enabling diagnostics) and that the method is capable of classifying recorded ECG signals to periods of fetal movement or rest (p < 0.01). This information on fetal movement can also serve as a valuable diagnostic tool.

关键词： Biomedical signal processing electrocardiography expectation-maximization algorithms fetal movement loop alignment vector cardiogram

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Estimating the parameters of the Marshall-Olkin bivariate Weibull distribution by EM algorithm

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第4期53卷 956-965页

作者： Kundu, Debasis Dey, Arabin Kumar Indian Inst Technol Dept Math & Stat Kanpur 208016 Uttar Pradesh India

in this paper we consider the Marshall-Olkin bivariate Weibull distribution. The Marshall-Olkin bivariate Weibull distribution is a singular distribution, whose both the marginals are univariate Weibull distributions. This is a generalization of the Marshall-Olkin bivariate exponential distribution. The cumulative joint distribution of the Marshall-Olkin bivariate Weibull distribution is a mixture of an absolute continuous distribution function and a singular distribution function. This distribution has four unknown parameters and it is observed that the maximum likelihood estimators of the unknown parameters cannot be obtained in explicit forms. In this paper we discuss about the computation of the maximum likelihood estimators of the unknown parameters using P EM algorithm. We perform some simulations to see the performances of the EM algorithm and re-analyze one data set for illustrative purpose. (C) 2008 Elsevier B.V. All rights reserved.

关键词： WEIBULL distribution PARAMETER estimation VARIABLES (Mathematics) GENERALIZABILITY theory (Statistics) expectation-maximization algorithms SIMULATION methods & models

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Learning the Dynamics of Arterial Traffic From Probe Data Using a Dynamic Bayesian Network

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2012年第4期13卷 1679-1693页

作者： Hofleitner, Aude Herring, Ryan Abbeel, Pieter Bayen, Alexandre Univ Calif Berkeley Berkeley CA 94720 USA Univ Calif Berkeley Calif Ctr Innovat Transportat Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. Sparse probe data represent the vast majority of the data available on arterial roads. This paper proposes a probabilistic modeling framework for estimating and predicting arterial travel-time distributions using sparsely observed probe vehicles. We introduce a model based on hydrodynamic traffic theory to learn the density of vehicles on arterial road segments, illustrating the distribution of delay within a road segment. The characterization of this distribution is essentially to use probe vehicles for traffic estimation: Probe vehicles report their location at random locations, and the travel times between location reports must be properly scaled to match the map discretization. A dynamic Bayesian network represents the spatiotemporal dependence on the network and provides a flexible framework to learn traffic dynamics from historical data and to perform real-time estimation with streaming data. The model is evaluated using data from a fleet of 500 probe vehicles in San Francisco, CA, which send Global Positioning System (GPS) data to our server every minute. The numerical experiments analyze the learning and estimation capabilities on a subnetwork with more than 800 links. The sampling rate of the probe vehicles does not provide detailed information about the location where vehicles encountered delay or the reason for any delay (i.e., signal delay, congestion delay, etc.). The model provides an increase in estimation accuracy of 35% when compared with a baseline approach to process probe-vehicle data.

关键词： expectation-maximization algorithms probes queuing analysis real-time systems statistical learning

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Robust probabilistic PCA with missing data and contribution analysis for outlier detection

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第10期53卷 3706-3716页

作者： Chen, Tao Martin, Elaine Montague, Gary Nanyang Technol Univ Sch Chem & Biomed Engn Singapore 637459 Singapore Univ Newcastle Sch Chem Engn & Adv Mat Newcastle Upon Tyne NE1 7RU Tyne & Wear England

Principal component analysis (PCA) is a widely adopted multivariate data analysis technique, with interpretation being established on the basis of both classical linear projection and a probability model (i.e. probabilistic PCA (PPCA)). Recently robust PPCA models, by using the multivariate t-distribution, have been proposed to consider the situation where there may be outliers within the data set. This paper presents an overview of the robust PPCA technique, and further discusses the issue of missing data. An expectation-maximization (EM) algorithm is presented for the maximum likelihood estimation of the model parameters in the presence of missing data. When applying robust PPCA for outlier detection, a contribution analysis method is proposed to identify which variables contribute the most to the occurrence of outliers, providing valuable information regarding the source of outlying data. The proposed technique is demonstrated on numerical examples, and the application to outlier detection and diagnosis in an industrial fermentation process. (C) 2009 Elsevier B.V. All rights reserved.

关键词： PRINCIPAL components analysis MISSING data (Statistics) MULTIVARIATE analysis PROBABILITY measures expectation-maximization algorithms ROBUST control NUMERICAL analysis FERMENTATION

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A NOTE ON STOPPING RULES IN EM-ML RECONSTRUCTIONS OF ECT IMAGES

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1994年第3期13卷 569-571页

作者： JOHNSON, VE Institute of Statistics and Decision Sciences Duke University Durham NC USA

The use of the expectation-maximization algorithm to obtain pseudo-maximum likelihood estimates (i.e. the EM-ML algorithm) of radiopharmaceutical distributions based on data collected from emission computed tomography (ECT) systems is now a well developed area, as witnessed by a number of recent articles on that topic, including the detailed study of the relative performance of EM-ML and FBP reconstructions provided in J. Llacer et al. (ibid., vol. 12, p. 215-31, 1993). However, there remains considerable confusion in the field regarding appropriate stopping rules for EM-ML algorithms, and in this correspondence the author attempts to detail a shortcoming of one of the more recent and innovative stopping rule criteria. In particular, the author discusses the effect of total photon counts on stopping criteria based on cross-validation.< >

关键词： Image reconstruction Electrical capacitance tomography expectation-maximization algorithms Computed tomography Positron emission tomography Imaging phantoms Probability Artificial intelligence Statistics Biomedical imaging

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Estimation of free flow speed and critical density in a segmented freeway using missing data and Monte Carlo-based expectation maximisation algorithm

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IET CONTROL THEORY AND APPLICATIONS 2011年第1期5卷 123-130页

作者： Ramezani, A. Moshiri, B. Abdulhai, B. Kian, A. R. Univ Tehran Control & Intelligent Proc Ctr Excellence Sch Elect & Comp Engn Tehran Iran Univ Toronto Dept Civil Engn Toronto ITS Ctr Toronto ON Canada

This study is concerned with the estimation of two key parameters in a stochastic non-linear second-order state-space model of traffic flow using the maximum likelihood approach while employing a recursive Monte Carl-based filtering and smoothing to solve related expectation maximisation (EM) algorithm. A maximum likelihood (ML) framework is employed in the interests of statistical efficiency. EM algorithm may be used to compute these ML estimates and Monte Carlo approach is used to compute required conditional expectations. Considered parameters, free flow speed and critical density are traffic flow characteristics which are key parameters used for traffic control, ramp metering, incident management etc. A set of field traffic data from the Interstate-494 highway located in Metro Freeway Network Area at Minnesota is used to demonstrate the effectiveness of the proposed approach.

关键词： TRAFFIC flow EXPRESS highways MONTE Carlo method STOCHASTIC analysis FILTERING of information expectation-maximization algorithms MAXIMUM likelihood statistics PARAMETER estimation STATE-space methods NONLINEAR models (Statistics)

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