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How to handle missing data in regression models using information criteria

STATISTICA NEERLANDICA 2011年第4期65卷 489-506页

作者： Kuiper, Rebecca M. Hoijtink, Herbert Univ Utrecht Dept Methodol & Stat NL-3508 TC Utrecht Netherlands

An important application of multiple regression is predictor selection. When there are no missing values in the data, information criteria can be used to select predictors. For example, one could apply the small-sample-size corrected version of the Akaike information criterion (AIC), the (AICC). In this article, we discuss how information criteria should be calculated when the dependent variable and/or the predictors contain missing values. Therewith, we extensively discuss and evaluate three models that can be employed to deal with the missing data, that is, to predict the missing values. The most complex model, that is, the model with all available predictors, outperforms the other models. These results also apply to more general hypotheses than predictor selection and also to structural equation modeling (SEM) models.

关键词： Akaike information criterion expectation-maximization algorithm information criteria missing data model selection multiple regression second-order AIC

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Online EM algorithm for Hidden Markov Models

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2011年第3期20卷 728-749页

作者： Cappe, Olivier Telecom ParisTech LTCI F-75634 Paris France CNRS F-75634 Paris France

Online (also called "recursive" or "adaptive") estimation of fixed model parameters in hidden Markov models is a topic of much interest in times series modeling. In this work, we propose an online parameter estimation algorithm that combines two key ideas. The first one, which is deeply rooted in the expectation-maximization (EM) methodology, consists in reparameterizing the problem using complete-data sufficient statistics. The second ingredient consists in exploiting a purely recursive form of smoothing in HMMs based on an auxiliary recursion. Although the proposed online EM algorithm resembles a classical stochastic approximation (or Robbins-Monro) algorithm, it is sufficiently different to resist conventional analysis of convergence. We thus provide limited results which identify the potential limiting points of the recursion as well as the large-sample behavior of the quantities involved in the algorithm. The performance of the proposed algorithm is numerically evaluated through simulations in the case of a noisily observed Markov chain. In this case, the algorithm reaches estimation results that are comparable to those of the maximum likelihood estimator for large sample sizes. The supplemental material for this article available online includes an appendix with the proofs of Theorem 1 and Corollary 1 stated in Section 4 as well as the MATLAB/OCTAVE code used to implement the algorithm in the case of a noisily observed Markov chain considered in Section 5.

关键词： expectation-maximization algorithm Online estimation Recursive estimation Smoothing Stochastic approximation

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EM algorithm with Improvement for Color Image Segmentation in Multiple Color Spaces

EM Algorithm with Improvement for Color Image Segmentation i...

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IEEE International Conference on Electronics, Communications and Control (ICECC)

作者： Zhang, Yonghong Zhang, Yongqin Shanghai Second Polytech Univ Practicing & Training Ctr Shanghai Peoples R China North China Univ Water Resources & Elect Power Sch Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9781457703218

The goal of image segmentation in imaging science is to solve the problem of partitioning an image into smaller disjoint homogeneous regions that share similar attributes. The novel technique of the expectation-maximization (EM) algorithm based on principal component analysis (PCA) with adaptively selecting dominant factors for color image segmentation in color spaces is studied here. And simultaneously, the final segmentation is completed by a simple labeling scheme. Then the comparative study of the refined EM algorithm is done in multiple color spaces. The experimental results from Berkeley segmentation dataset, illustrate that the improved EM algorithm with or without PCA has good segmentation results with fine adaptability in RGB, CIE XYZ, HSV, YCbCr, and YIQ(NTSC) color spaces where the results of test image changes little. Moreover, the optimized EM algorithm without PCA usually has better segmentation than the one with PCA. Nevertheless, these color spaces, i.e. CIE L*a*b*, and h1h2h3, usually produce poor segmentation on the reliability and accuracy of a set of test images by performance analysis with subjective and objective evaluation indicators.

关键词： expectation-maximization algorithm principal component analysis image segmentation color space color perception

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Detection of Resistivity for Antibiotics by Probabilistic Neural Networks

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JOURNAL OF MEDICAL SYSTEMS 2011年第1期35卷 87-91页

作者： Budak, Fatma Ubeyli, Elif Derya TOBB Ekon & Teknol Univ Fac Engn Dept Elect & Elect Engn TR-06530 Ankara Turkey Kocaeli Univ Fac Med Dept Microbiol TR-41380 Kocaeli Turkey

This paper presents the use of probabilistic neural networks (PNNs) for detection of resistivity for antibiotics (resistant and sensitive). The PNN is trained on the resistivity or sensitivity to the antibiotics of each record in the Salmonella database. Estimation of the whole parameter space for the PNN was performed by the maximum-likelihood (ML) estimation method. The expectation-maximization (EM) approach can help to achieve the ML estimation via iterative computation. Resistivity and sensitivity of the three antibiotics (ampicillin, chloramphenicol disks and trimethoprim-sulfamethoxazole) were classified with high accuracies by the PNN. The obtained results demonstrated the success of the PNN to help in detection of resistivity for antibiotics.

关键词： Probabilistic neural network expectation-maximization algorithm Resistivity to antibiotics Sensitivity to antibiotics Classification accuracy

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Low-Complexity Frequency Offset and Phase Noise Estimation for Burst-Mode Digital Transmission

Low-Complexity Frequency Offset and Phase Noise Estimation f...

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22nd IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Bhatti, Jabran Noels, Nele Moeneclaey, Marc Ghent State Univ Dept Telecommun & Informat Proc Ghent Belgium

ISBN: (纸本)9781457713484

The presence of a frequency offset (FO) and phase noise can cause severe performance degradation in digital communication systems. This work combines a simple FO estimation technique with a low-complexity phase noise estimation method, inspired by the space-alternating generalized expectation-maximization algorithm. Using a truncated discrete-cosine transform (DCT) expansion, the phase noise estimate is derived from the estimated DCT coefficients of the phase. A number of implementations of the proposed algorithm are discussed. Numerical results indicate that when estimating the FO from pilot symbols only, comparable performance can be reached as the computationally more complex case where the FO is updated iteratively, with small convergence time. The phase noise estimation step is well capable of compensating for the residual FO. For the considered scenario, performing FO compensation before iterative phase noise estimation yields a bit-error rate performance degradation close to the case where the FO is known.

关键词： Bit error rate DCT coefficient Degradation Discrete cosine transforms Estimation FO estimation technique Frequency estimation Phase noise Vectors bit error rate performance degradation burst-mode digital transmission communication complexity convergence of numerical methods convergence time digital communication digital communication system discrete cosine transforms error statistics expectation-maximisation algorithm expectation-maximization algorithm iterative methods iterative phase noise estimation low-complexity frequency offset low-complexity phase noise estimation method performance degradation phase noise truncated discrete-cosine transform expansion

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Model-based clustering with Hidden Markov Model regression for time series with regime changes

Model-based clustering with Hidden Markov Model regression f...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chamroukhi, Faicel Same, Allou Aknin, Patrice Govaert, Gerard Univ Paris 13 Comp Sci Lab CNRS LIPNUMR 7030 F-93430 Villetaneuse France IFSTTAR Research Unit UPE GRETTIA Boulogne France CNRS UMR 6599 Heudiasyc Lab Paris France

ISBN: (纸本)9781424496365

This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each cluster activates successively several polynomial regimes during time. The parameter estimation is performed by the maximum likelihood method through a dedicated expectation-maximization (EM) algorithm. The proposed approach is evaluated using simulated time series and real-world time series issued from a railway diagnosis application. Comparisons with existing approaches for time series clustering, including the stand EM for Gaussian mixtures, K-means clustering, the standard mixture of regression models and mixture of Hidden Markov Models, demonstrate the effectiveness of the proposed approach.

关键词： Clustering algorithms Data models Gaussian mixtures Hidden Markov models K-means clustering Markov processes Polynomials Spline Time series analysis expectation-maximisation algorithm expectation-maximization algorithm hidden Markov model regression hidden Markov models maximum likelihood method model-based clustering parameter estimation pattern clustering polynomial regressions railway diagnosis railway engineering regression analysis time series time series clustering

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Assessment of School Performance Through a Multilevel Latent Markov Rasch Model

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JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS 2011年第4期36卷 491-522页

作者： Bartolucci, Francesco Pennoni, Fulvia Vittadini, Giorgio Univ Perugia Dept Econ Finance & Stat I-06123 Perugia Italy Univ Milano Bicocca Dept Stat I-20126 Milan Italy Univ Milano Bicocca Dept Quantitat Methods Business & Econ Sci I-20126 Milan Italy Univ Milano Bicocca Interuniv Res Ctr Publ Serv CRISP I-20126 Milan Italy

An extension of the latent Markov Rasch model is described for the analysis of binary longitudinal data with covariates when subjects are collected in clusters, such as students clustered in classes. For each subject, a latent process is used to represent the characteristic of interest (e.g., ability) conditional on the effect of the cluster to which he or she belongs. The latter effect is modeled by a discrete latent variable associated to each cluster. For the maximum likelihood estimation of the model parameters, an expectation-maximization algorithm is outlined. Through the analysis of a data set collected in the Lombardy Region (Italy), it is shown how the proposed model may be used for assessing the development of cognitive achievement. The data set is based on test scores in mathematics observed over 3 years on middle school students attending public and non-state schools.

关键词： binary longitudinal data cognitive achievement expectation-maximization algorithm school effectiveness

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Autocorrelation Features for Synthetic Aperture Sonar Image Seabed Segmentation

Autocorrelation Features for Synthetic Aperture Sonar Image ...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Cobb, J. Tory Principe, Jose USN Warfare Ctr Panama City Div Panama City FL USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

ISBN: (纸本)9781457706530

High-resolution synthetic aperture sonar (SAS) systems yield richly detailed images of seabed environments. algorithms that automatically segment and label seabed textures such as coral, sea grass, sand ripple, and mud, require suitable features that discriminate between the texture classes. Here we present a robust, parameterized SAS image texture model based on the autocorrelation function (ACF) of the intensity image. This ACF texture model has been shown to accurately model first-and second-order statistical features of various seabed environments. An unsupervised multi-class k-means segmentation algorithm that uses the features derived from the ACF model is employed to label rock and ripple textures from a set of textured SAS images. The results of the segmentation are compared against the performance of the segmentation approach using biorthogonal wavelets and Haralick features. In the described experiments, the ACF model features are shown to produce better segmentations than the features based on wavelet coefficients and Haralick features for classifiers of low complexity.

关键词： Autocorrelation expectation-maximization algorithm image segmentation synthetic aperture sonar wavelet coefficients

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Identification of Nuclear Magnetic Resonance Signals via Gaussian Mixture Decomposition

Identification of Nuclear Magnetic Resonance Signals via Gau...

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10th International Symposium on Intelligent Data Analysis

作者： Krone, Martin Klawonn, Frank Luehrs, Thorsten Ritter, Christiane Ostfalia Univ Appl Sci Dept Comp Sci Salzdahlumer Str 46-48 D-38302 Wolfenbuettel Germany Helmholtz Ctr Infect Res Div Struct Biol D-38124 Braunschweig Germany

ISBN: (纸本)9783642247996

Nuclear Magnetic Resonance spectroscopy is a powerful technique for the determination of protein structures and has been supported by computers for decades. One important step during this process is the identification of resonances in the data. However, due to noise, overlapping effects and artifacts occuring during the measurements, many algorithms fail to identify resonances correctly. In this paper, we present a novel interpretation of the data as a sample drawn from a mixture of bi-variate Gaussian distributions. Therefore, the identification of resonances can be reduced to a Gaussian mixture decomposition problem which is solved with the help of the expectation-maximization algorithm. A program in the Java programming language that exploits an implementation of this algorithm is described and tested on experimental data. Our results indicate that this approach offers valuable information such as an objective measure on the likelihood of the identified resonances.

关键词： Nuclear Magnetic Resonance spectroscopy Gaussian mixture decomposition expectation-maximization algorithm

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GAUSSIAN MODELING OF MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN (HR-NMF)

GAUSSIAN MODELING OF MIXTURES OF NON-STATIONARY SIGNALS IN T...

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

作者： Badeau, Roland Telecom ParisTech Inst Telecom CNRS LTCI F-75634 Paris 13 France

ISBN: (纸本)9781457706936

Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of non-stationary signals in the Time-Frequency (TF) domain. However, unlike the High Resolution (HR) methods dedicated to mixtures of exponentials, its spectral resolution is limited by that of the underlying TF representation. In this paper, we propose a unified probabilistic model called HR-NMF, that permits to overcome this limit by taking both phases and local correlations in each frequency band into account. This model is estimated with a recursive implementation of the EM algorithm, that is successfully applied to source separation and audio inpainting.

关键词： Nonnegative Matrix Factorization High Resolution methods expectation-maximization algorithm source separation audio inpainting

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