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Multivariate asset return prediction with mixture models

EUROPEAN JOURNAL OF FINANCE 2015年第13-14期21卷 1214-1252页

作者： Paolella, Marc S. Univ Zurich Swiss Banking Inst CH-8006 Zurich Switzerland Swiss Finance Inst CH-8006 Zurich Switzerland

The use of mixture distributions for modeling asset returns has a long history in finance. New methods of demonstrating support for the presence of mixtures in the multivariate case are provided. The use of a two-component multivariate normal mixture distribution, coupled with shrinkage via a quasi-Bayesian prior, is motivated, and shown to be numerically simple and reliable to estimate, unlike the majority of multivariate GARCH models in existence. Equally important, it provides a clear improvement over use of GARCH models feasible for use with a large number of assets, such as constant conditional correlation, dynamic conditional correlation, and their extensions, with respect to out-of-sample density forecasting. A generalization to a mixture of multivariate Laplace distributions is motivated via univariate and multivariate analysis of the data, and an expectation-maximization algorithm is developed for its estimation in conjunction with a quasi-Bayesian prior. It is shown to deliver significantly better forecasts than the mixed normal, with fast and numerically reliable estimation. Crucially, the distribution theory required for portfolio theory and risk assessment is developed.

关键词： density forecasting expectation-maximization algorithm fat tails mixture distributions multivariate Laplace distribution quasi-Bayesian estimation shrinkage estimation weighted likelihood

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A mixture of experts network structure for breast cancer diagnosis

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Journal of Medical Systems 2005年第5期29卷 569-579页

作者： Übeyli, Elif Derya Department of Electrical and Electronics Engineering Faculty of Engineering TOBB Ekonomi Ve Teknoloji Üniversitesi Çankaya Ankara Sogutozu 06560 Turkey

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide diagnosing of breast cancer. expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. Specifically, diagnosis is an attempt to accurately forecast the outcome of a specific situation, using as input information obtained from a concrete set of variables that potentially describe the situation. The ME network structure was implemented for breast cancer diagnosis using the attributes of each record in the Wisconsin breast cancer database. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. For the Wisconsin breast cancer diagnosis problem, the obtained total classification accuracy by the ME network structure was 98.85%. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. © 2005 Springer Science+Business Media, Inc.

关键词： Breast cancer diagnosis Diagnostic accuracy expectation-maximization algorithm Mixture of experts

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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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Maximum Likelihood Estimation of the Flow Size Distribution Tail Index from Sampled Packet Data 09

Maximum Likelihood Estimation of the Flow Size Distribution ...

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Joint International Conference on Measurement and Modeling of Computer Systems

作者： Loiseau, Patrick Goncalves, Paulo Girard, Stephane Forbes, Florence Primet, Pascale Vicat-Blanc Univ Lyon ENS Lyon Lyon France

ISBN: (纸本)9781605585116

In the context of network traffic analysis, we address the problem of estimating the tail index of flow (or more generally of any group) size distribution from the observation of a, sampled population of packets (individuals). We give an exhaustive bibliography of the existing methods and show the relations between them. The main contribution of this work is then to propose a new method to estimate the tail index from sampled data, based on the resolution of the maximum likelihood problem. To assess the performance of our method, we present a full performance evaluation based on numerical simulations, and also on a real traffic trace corresponding to internet traffic recently acquired.

关键词： Network Monitoring Traffic Measurement Heavy-Tailed Distribution Packet Sampling Maximum Likelihood Estimation expectation-maximization algorithm

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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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Modeling Multi-source Remote Sensing Image Classifier Based on the MDL Principle: Experimental Studies

Modeling Multi-source Remote Sensing Image Classifier Based ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xia, Huaiying Hu, Rukun Xu, Bingxin Guo, Ping Beijing Normal Univ Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9781424465880

In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.

关键词： minimum description length remote sensing image classification technique mixture model 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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A Bayesian Model for Opening Prediction in RTS Games with Application to StarCraft

A Bayesian Model for Opening Prediction in RTS Games with Ap...

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IEEE Conference on Computational Intelligence and Games (CIG)

作者： Synnaeve, Gabriel Bessiere, Pierre

ISBN: (纸本)9781457700095

This paper presents a Bayesian model to predict the opening (first strategy) of opponents in real-time strategy (RTS) games. Our model is general enough to be applied to any RTS game with the canonical gameplay of gathering resources to extend a technology tree and produce military units and we applied it to StarCraft1. This model can also predict the possible technology trees of the opponent, but we will focus on openings here. The parameters of this model are learned from replays (game logs), labeled with openings. We present a semi-supervised method of labeling replays with the expectation-maximization algorithm and key features, then we use these labels to learn our parameters and benchmark our method with cross-validation. Uses of such a model range from a commentary assistant (for competitive games) to a core component of a dynamic RTS bot/AI, as it will be part of our StarCraft AI competition entry bot.

关键词： Bayes methods Bayesian methods Bayesian model Buildings Games Hidden Markov models Labeling Predictive models RTS game StarCraft commentary assistant competitive game computer games expectation-maximisation algorithm expectation-maximization algorithm opening prediction real-time strategy game semisupervised method

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Learning marked Markov modulated Poisson processes for online predictive analysis of attack scenarios 30

Learning marked Markov modulated Poisson processes for onlin...

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30th IEEE International Symposium on Software Reliability Engineering (ISSRE)

作者： Carnevali, Laura Santoni, Francesco Vicario, Enrico Univ Florence Dept Informat Engn Via Santa Marta 3 I-50139 Florence Italy

ISBN: (纸本)9781728149820

Runtime predictive analysis of quantitative models can support software reliability in various application scenarios. The spread of logging technologies promotes approaches where such models are learned from observed events. We consider a system visiting transient states of a hidden process until reaching a final state and producing observations with stochastic arrival times and types conditioned by visited states, and we abstract it as a marked Markov modulated Poisson Process (MMMPP) with left-to right structure. We present an expectation-maximization (EM) algorithm that learns the MMMPP parameters from observation sequences acquired in repeated execution of the transient behavior, and we use the model at runtime to infer the current state of the process from actual observed events and to dynamically evaluate the remaining time to the final state. The approach is illustrated using synthetic datasets generated from a stochastic attack tree of the literature enriched with an observation model associating each state with an expected statistics of observation types and arrival times. Accuracy of prediction is evaluated under different variability of hidden states sojourn durations and of the observations arrival process, and compared against previous literature that mainly exploits either the timing or the types of observed events.

关键词： Marked Markov Modulated Poisson Processes expectation-maximization algorithm online failure prediction state-dependent event types and times attack trees

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A theoretical assessment of image analysis: Statistical vs structural approaches

A theoretical assessment of image analysis: Statistical vs s...

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Medical Imaging 2003 Conference

作者： Lei, TH Udupa, JK Univ Penn Dept Radiol MIPG Philadelphia PA 19104 USA

ISBN: (纸本)0819448338

Statistical and structural methods axe two major approaches commonly used in image analysis and have demonstrated considerable success. The former is based on statistical properties and stochastic models of the image and the latter utilizes geometric and topological models. In this study, Markov random field (MRF) theory / model based image segmentation and Fuzzy Connectedness (FC) theory / Fuzzy connected object delineation are chosen as the representatives for these two approaches, respectively. The comparative study is focused on their theoretical foundations and main operative procedures. The MRF is defined on a lattice and the associated neighborhood system and is based on the Markov property. The FC method is defined on a fuzzy digital space and is based on fuzzy relations. Locally, MRF is characterized by potentials of cliques, and FC is described by fuzzy adjacency and affinity relations. Globally, MRF is characterized by Gibbs distribution, and FC is described by fuzzy connectedness. The task of MRF model based image segmentation is to seek a realization of the embedded MRF through a two-level operation: partitioning and labeling. The task of FC object delineation is to extract a fuzzy object from a given scene, through a two-step operation: recognition and delineation. Theoretical foundations which underly statistical and structural approaches and the principles of the main operative procedures in image segmentation by these two approaches demonstrate more similarities than differences between them. Two approaches can also complement each other, particularly in seed selection, scale formation, affinity and object membership function design for FC and neighbor set selection and clique potential design for MRF.

关键词： Markov random field neighborhood system clique potential fuzzy connectedness adjacency affinity image segmentation expectation-maximization algorithm Iterated Conditional Mode dynamic programming

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