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An online outlier-robust extended Kalman filter via em-algorithm for ship maneuvering data

MEASURemENT 2025年 250卷

作者： Yue, Wancheng Ren, Junsheng Bai, Weiwei Dalian Maritime Univ Key Lab Marine Simulat & Control Dalian 116026 Peoples R China

This paper aims to enhance the accuracy and reliability of ship identification processes by preprocessing noise and outliers in the collected data. We propose a preprocessing method using the Expectation-Maximization (em) algorithm and the Extended Kalman Filter (EKF) applied to ship trajectory and motion data, demonstrating significant advancements over existing methodologies. We introduce a novel application of one-dimensional Student's t-distributions with independent parameters by Q and R (measured noise covariance), enabling more precise modeling of sensor noise and outliers, thus improving data processing robustness. Our algorithm also incorporates adaptive learning capabilities, allowing adjustments to various environmental conditions, which enhances noise and outlier handling and broadens applicability across real-world scenarios. Experimental results indicate that our methods significantly improve data accuracy and ship identification reliability, underscoring their practical significance. These techniques provide robust technical support for accurate ship motion information and have considerable potential for maritime identification applications.

关键词： Student-t noises Outliers em-algorithm Kalman filter Robust

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Student-t stochastic volatility model with composite likelihood em-algorithm

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JOURNAL OF TIME SERIES ANALYSIS 2023年第1期44卷 125-147页

作者： Sundararajan, Raanju R. Barreto-Souza, Wagner Southern Methodist Univ Dept Stat Sci Dallas TX USA King Abdullah Univ Sci & Technol Stat Program Thuwal Saudi Arabia

A new robust stochastic volatility (SV) model having Student-t marginals is proposed. Our process is defined through a linear normal regression model driven by a latent gamma process that controls temporal dependence. This gamma process is strategically chosen to enable us to find an explicit expression for the pairwise joint density function of the Student-t response process. With this at hand, we propose a composite likelihood (CL) based inference for our model, which can be straightforwardly implemented with a low computational cost. This is a remarkable feature of our Student-t process over existing SV models in the literature that involve computationally heavy algorithms for estimating parameters. Aiming at a precise estimation of the parameters related to the latent process, we propose a CL expectation-maximization algorithm and discuss a bootstrap approach to obtain standard errors. The finite-sample performance of our CL methods is assessed through Monte Carlo simulations. The methodology is motivated by an empirical application in the financial market. We analyze the relationship, across multiple time periods, between various US sector Exchange-Traded Funds returns and individual companies' stock price returns based on our novel Student-t model. This relationship is further utilized in selecting optimal financial portfolios. Generalizations of the Student-t SV model are also proposed.

关键词： Composite likelihood estimation em-algorithm exchange-traded funds quantitative trading strategies stochastic volatility models robustness

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A contrast of EB, modal and em-algorithm estimates arising in the one-way analysis of variance situation

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STATISTICS & PROBABILITY LETTERS 2000年第1期48卷 33-58页

作者： Guttman, I SUNY Buffalo Dept Math Buffalo NY 14214 USA

This paper explores the question of the choice of whether to use a joint posterior or a marginal posterior as basis for analysis with respect to various parameters of interest. It turns out that in the one-way analysis of variance situation there is an optimal choice. Connections with empirical Bayes estimation is stressed as well as the use of the em-algorithm. We demonstrate that for the latter, care must be taken with the choice of parameters to be declared "missing", for a wrong choice could lead to inconsistent estimators or estimators with poor mean-square behaviour. The discussion is in the context of one-way analysis of variance. (C) 2000 Published by Elsevier Science B.V. All rights reserved.

关键词： analysis of variance posterior analysis modal estimation empirical bayes estimates em-algorithm

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Estimation in the Birnbaum-Saunders distribution based on scale-mixture of normals and the em-algorithm

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SORT 2009年第2期33卷 171-192页

作者： Balakrishnan, N. Leiva, Víctor Sanhueza, Antonio Vilca, Filidor Departamento de Estadística Universidad de Valparaíso Casilla 5030 Valparaíso Chile Department of Mathematics and Statistics McMaster University Hamilton ON Canada Departamento de Estadística CIMFAV Universidad de Valparaíso Valparaíso Chile Departamento de Matemática y Estadística Universidad de la Frontera Temuco Chile Departamento de Estatística Universidade Estadual de Campinas São Paulo Brazil

Scale mixtures of normal (SMN) distributions are used for modeling symmetric data. Members of this family have appealing properties such as robust estimates, easy number generation, and efficient computation of the ML estimates via the em-algorithm. The Birnbaum-Saunders (BS) distribution is a positively skewed model that is related to the normal distribution and has received considerable attention. We introduce a type of BS distributions based on SMN models, produce a lifetime analysis, develop the em-algorithm for ML estimation of parameters, and illustrate the obtained results with real data showing the robustness of the estimation procedure. Birnbaum-Saunders distribution, em-algorithm, kurtosis, maximum likelihood methods, robust estimation, scale mixtures of normal distributions.

关键词： Birnbaum-Saunders distribution em-algorithm Kurtosis Maximum likelihood methods Robust estimation Scale mixtures of normal distributions

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Cox regression with incomplete covariate measurements using the em-algorithm

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SCANDINAVIAN JOURNAL OF STATISTICS 1999年第4期26卷 479-491页

作者： Martinussen, T Royal Vet & Agr Univ Dept Math & Phys DK-1871 Frederiksberg C Denmark

Ibrahim (1990) used the em-algorithm to obtain maximum likelihood estimates of the regression parameters in generalized linear models with partially missing covariates. The technique was termed em by the method of weights. In this paper, we generalize this technique to Cox regression analysis with missing values in the covariates. We specify a full model letting the unobserved covariate values be random and then maximize the observed likelihood. The asymptotic covariance matrix is estimated by the inverse information matrix. The missing data are allowed to be missing at random but also the nonignorable non-response situation may in principle be considered. Simulation studies indicate that the proposed method is more efficient than the method suggested by Paik & Tsai (1997). We apply the procedure to a clinical trials example with six covariates,vith three of them having missing values.

关键词： Cox analysis em-algorithm missing covariate data survival analysis

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A NOTE ON THE MISSING VALUE PRINCIPLE AND THE em-algorithm FOR ESTIMATION AND PREDICTION IN SAMPLING FROM FINITE POPULATIONS WITH A MULTINORMAL SUPERPOPULATION MODEL

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STATISTICS & PROBABILITY LETTERS 1986年第1期4卷 35-37页

作者： ZACKS, S RODRIGUEZ, J UNIV SAO PAULO SAO PAULO SP BRAZIL

It is shown that both the missing value principle (MVP) of Orchard and Woodbury (1972) and the em-algorithm of Dempster, Laird and Rubin (1972) yield a unique predictor of the population total, under a superpopulation... 详细信息

关键词： 62D05 missing value principle em-algorithm finite populations multinormal superpopulation

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Maximum likelihood estimation for tied survival data under Cox regression model via em-algorithm

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LIFETIME DATA ANALYSIS 2007年第3期13卷 399-420页

作者： Scheike, Thomas H. Sun, Yanqing Univ N Carolina Dept Math & Stat Charlotte NC 28223 USA Univ Copenhagen Dept Biostat DK-1014 Copenhagen K Denmark

We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using em-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. A simulation study is conducted to compare the finite sample properties of the methods.

关键词： Cox regression model tied survival data em-algorithm asymptotics

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Application of the em-algorithm for Bayesian Network Modelling to Improve Forest Growth Estimates

Application of the EM-algorithm for Bayesian Network Modelli...

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1st International Conference on Spatial Statistics - Mapping Global Change

作者： Mustafa, Y. T. Tolpekin, V. Stein, A. Univ Twente ITC Fac Geoinformat Sci & Earth Observat NL-7500 AE Enschede Netherlands

Leaf area index (LAI) is a biophysical variable that is related to atmosphere-biosphere exchange of CO2. One way to obtain LAI value is by the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products (LAI MODIS). The LAI MODIS has been used to improve the physiological principles predicting growth (3-PG) model within a Bayesian Network (BN) set-up. The MODIS time series, however, contains gaps caused by persistent clouds, cloud contamination, and other retrieval problems. We therefore formulated the em-algorithm to estimate the missing MODIS LAI values. The em-algorithm is applied to three different cases: successive and not successive two winter seasons, and not successive missing MODIS LAI during the time study of 26 successive months at which the performance of the BN is assessed. Results show that the MODIS LAI is estimated such that the maximum value of the mean absolute error between the original MODIS LAI and the estimated MODIS LAI by em-algorithm is 0.16. This is a low value, and shows the success of our approach. Moreover, the BN output improves when the em-algorithm is carried out to estimate the inconsecutive missing MODIS LAI such that the root mean square error reduces from 1.57 to 1.49. We conclude that the em-algorithm within a BN can handle the missing MODIS LAI values and that it improves estimation of the LAI. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Spatial Statistics 2011

关键词： em-algorithm Gaussian Bayesian networks (GBNs) leaf area index (LAI) Moderate Resolution Imaging Spectroradiometer (MODIS)

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High Dimension Finite Mixture Gaussian Model Estimation for Short Time Fourier Decomposition by em-algorithm

High Dimension Finite Mixture Gaussian Model Estimation for ...

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IEEE International Conference on Information and Automation

作者： Chen, Mei Liu, Yan Zhuang, Mingguang Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA Tongji Univ Sch Software Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9781424421831

A modification of the central limit theorem indicates that for a stationary or asymptotically stationary random process, its Fourier coefficients are independent complex Gaussian random variables [1]. We apply this idea in the short time Fourier transform, where most process has the asymptotic stationary property in short time sense. The estimated parameters of the complex Gaussian distribution can be used in the feature extraction or the plug-in hypothesis test for recognition. The problem becomes to estimate the parameters of the complex Gaussian. The windowed short time Fourier coefficients are not simple complex Gaussian but contaminated Gaussian, which means we need to estimate the parameters of mixture Gaussian. The em-algorithm could estimate the parameters directly but the M-step is still complicate. Recasting the contaminated Gaussian as a finite mixture Gaussian model, we can estimated the mean vector and covariance matrix for each time-frequency bin. Estimate the parameters of a mixture high-dimension joint Gaussian distribution with high accuracy and low computation cost shows a good way to solve the problem of distribution estimation. With the estimated distribution, we can create a statistical model for recognition. This method is examined with a mixture 2 dimension joint Gaussian distribution and the simulation results are discussed with good performance. The convergence preserved by the em-algorithm and the convergence rate is examined too.

关键词： short-time Fourier transform distribution estimation em-algorithm Finite Mixture Model

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Using the em-algorithm to Approximate the Distribution of a Mixture by Hyperexponents

Using the EM-algorithm to Approximate the Distribution of a ...

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International Conference on Engineering and Telecommunication (EnT)

作者： Buranova, Marina Ergasheva, Dinara Kartashevskiy, Vyacheslav Povolzhskiy State Univ Telecommunicat & Informat Samara Russia

ISBN: (纸本)9781728135649

Modern infocommunication networks are developing very intensively, the structure of networks and the nature of applications are changing, which leads to the need for new approaches to research the parameters of their functioning. The paper proposes the solution to the problem of approximating an arbitrary probability density in the G/G/1 system using hyperexponential distributions. Moreover, to determine the parameters of the probability density of the hyperexponential distribution, it is proposed to use the em algorithm. An example of approximation by a system of type H-2/D-2/1 of a real implementation of IP network traffic is given.

关键词： approximation queuing systems em-algorithm mixture distribution probability density modeling

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