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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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An Adaptive EM algorithm for NHPP Software Reliability Models 61

An Adaptive EM Algorithm for NHPP Software Reliability Model...

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61st Annual Reliability and Maintainability Symposium (RAMS)

作者： Nagaraju, Vidhyashree Fiondella, Lance Univ Massachusetts Dartmouth Dept Elect & Comp Engn 285 Old Westport Rd N Dartmouth MA 02747 USA

ISBN: (纸本)9781479967032

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGM) enable several quantitative metrics that can be used to guide important decisions during the software engineering life cycle such as testing resource allocation and release planning. However, many of these SRGM possess complex mathematical forms that make them difficult to apply in practice because traditional statistical procedures such as maximum likelihood estimation must solve a system of non-linear equations to identify the numerical parameters that best characterize a set of failure data. Recently, researchers have made significant progress in overcoming this difficulty by developing an expectation-maximization (EM) algorithm that exhibits better convergence properties and can therefore find the maximum likelihood estimates of complex SRGM with greater ease. This EM algorithm, however, assumes that some model parameters are constant and thus the approach is not capable of identifying the set of numerical parameters that maximize the likelihood function. This paper presents an adaptive EM algorithm to identify the maximum likelihood estimates of all parameters of multiple NHPP SRGM with complex mathematical forms. We illustrate our enhanced algorithm through a series of examples. The results show that the algorithm can efficiently identify the set of numerical parameters that globally maximizes the likelihood function. Thus, the adaptive algorithm can significantly simplify the application of complex SRGM.

关键词： expectation-maximization algorithm Non-homogeneous Poisson process Software reliability

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Data-Driven Statistical Analysis of Dynamic Vessel Trajectories in Wuhan Section of the Yangtze River 17

Data-Driven Statistical Analysis of Dynamic Vessel Trajector...

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International Conference on Big Data Research (ICBDR)

作者： Liang, Maohan Liu, Ryan Wen Li, Yan Wu, Jianhua Liu, Jingxian Wuhan Univ Technol Sch Nav Hubei Key Lab Inland Shipping Technol Wuhan 430063 Hubei Peoples R China

ISBN: (纸本)9781450353564

Automatic identification system (AIS), which records the spatio-temporal dynamic vessel trajectories, has recently attracted increasing attention due to its great potential in maritime management and ocean engineering. AIS has been successfully utilized to assist intelligent navigation and enhance transportation safety. To better understand the vessel behavior behind the massive AIS trajectories, it is necessary to analyze the statistical properties of AIS trajectories from different aspects. In this work, we mainly focus on the distribution of vessel speeds, distribution of spherical distances, distribution of longitude and latitude differences between successive trajectory points for both downstream and upstream vessels in Wuhan Section of the Yangtze River. To accurately approximate these distributions, Gaussian mixture model (GMM) was introduced to analyze the dynamic vessel trajectories. In particular, the optimal parameters of GMM were estimated using the iterative expectation-maximization (EM) algorithm. Experiments on massive realistic vessel trajectories have demonstrated that there exists significantly different distributions of vessel speeds, spherical distances, longitude and latitude differences between downstream and upstream vessels.

关键词： Trajectory mining Automatic identification system Gaussian mixture model expectation-maximization algorithm Statistical analysis

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New Concept of PLC Modems: Multi-Carrier System for Frequency Selective Slow-Fading Channels Based on Layered SCCC Turbocodes

New Concept of PLC Modems: Multi-Carrier System for Frequenc...

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作者： Zavrtalek, J. Brno University of Technology

The article introduces a novel concept of a PLC modem as a complement to the existing G3 and PRIME standards for communications using medium- or high-voltage overhead or cable lines. The proposed concept is based on the fact that the levels of impulse noise and frequency selectivity are lower on high-voltage lines than on low-voltage ones. Also, the demands for “cost-effective” circuitry design are not so crucial as in the case of modems for low-voltage level. In contract to these positive conditions, however, there is the need to overcome much longer distances and to take into account low SNR on the receiving side. With respect to the listed reasons, our concept makes use of MCM, instead of OFDM. The assumption of low SNR is compensated through the use of an efficient channel coding based on a serially concatenated turbo code. In addition, MCM offers lower latency and PAPR compared to OFDM. Therefore, when using MCM, it is possible to excite the line with higher power. The proposed concept has been verified during experimental transmission of testing data over a real, 5 km long, 22kV overhead line.

关键词： Power line communication multi-carrier modulation serially concatenated convolutional codes Iterative detection BCJR forward-backward algorithm soft-output Viterbi algorithm soft-in soft-out module expectation-maximization algorithm soft decision

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Variational Bayesian Inference for Multichannel Dereverberation and Noise Reduction

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2014年第8期22卷 1320-1335页

作者： Schmid, Dominic Enzner, Gerald Malik, Sarmad Kolossa, Dorothea Martin, Rainer Ruhr Univ Bochum Inst Commun Acoust D-44780 Bochum Germany

Room reverberation and background noise severely degrade the quality of hands-free speech communication systems. In this work, we address the problem of combined speech dereverberation and noise reduction using a variational Bayesian (VB) inference approach. Our method relies on a multichannel state-space model for the acoustic channels that combines frame-based observation equations in the frequency domain with a first-order Markov model to describe the time-varying nature of the room impulse responses. By modeling the channels and the source signal as latent random variables, we formulate a lower bound on the log-likelihood function of the model parameters given the observed microphone signals and iteratively maximize it using an online expectation-maximization approach. Our derivation yields update equations to jointly estimate the channel and source posterior distributions and the remaining model parameters. An inspection of the resulting VB algorithm for blind equalization and channel identification (VB-BENCH) reveals that the presented framework includes previously proposed methods as special cases. Finally, we evaluate the performance of our approach in terms of speech quality, adaptation times, and speech recognition results to demonstrate its effectiveness for a wide range of reverberation and noise conditions.

关键词： Dereverberation expectation-maximization algorithm noise reduction state-space model variational Bayes

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Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2014年 75卷 96-111页

作者： Zhang, Mimi Hu, Qingpei Xie, Min Yu, Dan City Univ Hong Kong Dept Syst Engn & Engn Management Kowloon Tong Hong Kong Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China

The aim of this paper is to propose an approach to constructing lower confidence limits for a reliability function and investigate the effect of a sampling scheme on the performance of the proposed approach. This is accomplished by using a data-completion algorithm and certain Monte Carlo methods. The data-completion algorithm fills in censored observations with pseudo-complete data while the Monte Carlo methods simulate observations for complicated pivotal quantities. The Birnbaum-Saunders distribution, the lognormal distribution and the Weibull distribution are employed for illustrative purpose. The results of three cases of data-analysis are presented to validate the applicability and effectiveness of the proposed methods. The first case is illustrated through simulated data, and the last two cases are illustrated through two real-data sets. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Data completion expectation-maximization algorithm Incomplete data Interval estimate Method of moments

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Source Coding with Side Information at the Decoder and Uncertain Knowledge of the Correlation

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IEEE TRANSACTIONS ON COMMUNICATIONS 2014年第1期62卷 269-279页

作者： Dupraz, Elsa Roumy, Aline Kieffer, Michel Univ Paris 11 L2S CNRS SUPELEC F-91192 Gif Sur Yvette France INRIA F-35042 Rennes France Inst Univ France F-75013 Paris France

This paper considers the problem of lossless source coding with side information at the decoder, when the correlation model between the source and the side information is uncertain. Four parametrized models representing the correlation between the source and the side information are introduced. The uncertainty on the correlation appears through the lack of knowledge on the value of the parameters. For each model, we propose a practical coding scheme based on non-binary Low Density Parity Check Codes and able to deal with the parameter uncertainty. At the encoder, the choice of the coding rate results from an information theoretical analysis. Then we propose decoding algorithms that jointly estimate the source vector and the parameters. As the proposed decoder is based on the expectation-maximization algorithm, which is very sensitive to initialization, we also propose a method to produce first a coarse estimate of the parameters.

关键词： Source coding with side information non-binary LDPC codes expectation-maximization algorithm

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Modeling temporal flow assignment in metro networks using smart card data

Modeling temporal flow assignment in metro networks using sm...

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International IEEE Conference on Intelligent Transportation Systems

作者： Lijun Sun Jian Gang Jin Future Cities Lab. Singapore-ETH Centre Singapore Singapore

ISBN: (纸本)9781467365970

Understanding passenger flow assignment patterns in a complex metro network is crucial to maintaining service reliability and developing efficient response during disruptions. In reality passengers' perception of different cost attributes may vary with time. This paper focuses on quantifying the temporal variation of passenger route choice behavior and its impact on overall passenger flow assignment. In order to efficiently estimate model parameters, we adapt a previous model to a missing data problem by introducing latent variables on route choice outcomes for each travel time observation. The revised model can be estimated using expectation-maximization (EM) algorithm. We apply the proposed framework on Singapore's metro system and temporal grouped smart card transactions. We find that route choice coefficients vary substantially with time. The relative value of transfer time in terms of in-vehicle time ranges from 2 to 3, being higher at off-peak hours than during morning/evening peaks. The result suggests that passenger care more about total travel time during peak hours, whereas comfort (e.g., less transfer time) is of more concern during off-peaks. The proposed framework is general and can be applied on other networks.

关键词： intelligent transportation systems public transport smart cards EM algorithm Singapore metro system expectation-maximization algorithm metro network passenger flow assignment patterns reliability smart card data temporal flow assignment temporal grouped smart card transaction Adaptation models Data models Estimation Reliability Smart cards Sun Tin Data-driven Flow assignment Metro network Route choice Smart card data

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An Adaptive EM algorithm for NHPP Software Reliability Models

An Adaptive EM Algorithm for NHPP Software Reliability Model...

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Reliability and Maintainability Symposium

作者： Vidhyashree Nagaraju Lance Fiondella Department of Electrical and Computer Engineering University of Massachusetts - Dartmouth

ISBN: (纸本)9781479967049

关键词： expectation-maximization algorithm Non-homogeneous Poisson process Software reliability

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Factor analysis parameter estimation from incomplete data

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2014年 70卷 61-66页

作者： Roberts, W. J. J. Opera Solutions Jersey City NJ 07302 USA

An expectation maximization (EM) algorithm for factor analysis parameter estimation when observations are missing is developed. In contrast to existing EM algorithms for this problem, the algorithm here is developed assuming the missing observations are not part of the complete data in the EM formulation. The resulting algorithm provides increased computational efficiency through sparse matrix operations. The algorithm is demonstrated on two sparse, high-dimensional data sets that are prohibitively large for existing algorithms: the Netflix movie recommendation data set and the Yahoo! musical item data set. The resulting factor models are applied to predict missing values using conditional mean estimation, achieving root mean square errors of 0.9001 and 24.08 on the Netflix and Yahoo! data sets, respectively. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Recommendation expectation-maximization algorithm Conditional mean

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