This paper addresses the problem of detecting anomalous activity in traffic networks where the network is not directly observed. Given knowledge of what the node-to-node traffic in a network should be, any activity th...
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This paper addresses the problem of detecting anomalous activity in traffic networks where the network is not directly observed. Given knowledge of what the node-to-node traffic in a network should be, any activity that differs significantly from this baseline would be considered anomalous. We propose a Bayesian hierarchical model for estimating the traffic rates and detecting anomalous changes in the network. The probabilistic nature of the model allows us to perform statistical goodness-of-fit tests to detect significant deviations from a baseline network. We show that due to the more defined structure of the hierarchical Bayesianmodel, such tests perform well even when the empirical models estimated by the em algorithm are misspecified. We apply our model to both simulated and real datasets to demonstrate its superior performance over existing alternatives.
Finite mixture of Gaussian distributions provide a flexible semiparametric methodology for density estimation when the continuous variables under investigation have no boundaries. However, in practical applications, v...
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Finite mixture of Gaussian distributions provide a flexible semiparametric methodology for density estimation when the continuous variables under investigation have no boundaries. However, in practical applications, variables may be partially bounded (e.g., taking nonnegative values) or completely bounded (e.g., taking values in the unit interval). In this case, the standard Gaussian finite mixture model assigns nonzero densities to any possible values, even to those outside the ranges where the variables are defined, hence resulting in potentially severe bias. In this paper, we propose a transformation-based approach for Gaussian mixture modeling in case of bounded variables. The basic idea is to carry out density estimation not on the original data but on appropriately transformed data. Then, the density for the original data can be obtained by a change of variables. Both the transformation parameters and the parameters of the Gaussian mixture are jointly estimated by the expectation-maximization (em) algorithm. The methodology for partially and completely bounded data is illustrated using both simulated data and real data applications.
Statistical emulators are used to approximate the output of complex physical models when their computational burden limits any sensitivity and uncertainty analysis of model output to variation in the model inputs. In ...
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Statistical emulators are used to approximate the output of complex physical models when their computational burden limits any sensitivity and uncertainty analysis of model output to variation in the model inputs. In this paper, we introduce a flexible emulator which is able to handle multivariate model outputs and missing values. The emulator is based on a spatial model and the D-STem software, which is extended to include emulator fitting using the em algorithm. The missing values handling capabilities of the emulator are exploited to keep the number of model output realisations as low as possible when the computing burden of each realisation is high. As a case study, we emulate the output of the Atmospheric Dispersion Modelling System (ADMS) used by the Scottish Environment Protection Agency (SEPA) to model the air quality of the city of Aberdeen (UK). With the emulator, we study the city air quality under a discrete set of realisations and identify conditions under which, with a given probability, the 40 mu g m(-3) yearly average concentration limit for NO2 of EU legislation is not exceeded at the locations of the city monitoring stations. The effect of missing values on the emulator estimation and probability of exceedances are studied by means of simulations.
This paper discusses regression analysis of interval-censored failure time data with a cured subgroup under a general class of semiparametric transformation cure models. For inference, a novel and stable expectation m...
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This paper discusses regression analysis of interval-censored failure time data with a cured subgroup under a general class of semiparametric transformation cure models. For inference, a novel and stable expectation maximization (em) algorithm with the use of Poisson variables is developed to overcome the difficulty in maximizing the observed data likelihood function with complex form. The asymptotic properties of the resulting estimators are established and in particular, the estimators of regression parameters are shown to be semiparametrically efficient. The numerical results obtained from a simulation study indicate that the proposed approach works well for practical situations. An application to a set of data on children's mortality is also provided. (C) 2018 Elsevier B.V. All rights reserved.
In the structural health monitoring or nondestructive examination system based on the Lamb wave technology, the accurate and valid characteristics of wave packet extracted from the signal are critical factors to evalu...
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In the structural health monitoring or nondestructive examination system based on the Lamb wave technology, the accurate and valid characteristics of wave packet extracted from the signal are critical factors to evaluate damage. However, the dispersion effect and the multi-mode characteristic in the elastic wave make the data extraction difficult and degrade the resolution, and therefore further prevent the effectiveness of Lamb wave for damage detection. In this study, we proposed a model-based approach for extracting effective characteristics from the noisy signals. By taking the narrow band Gabor pulse as the incident pulse and considering the general non-linear frequency dispersion (quadratic dispersion), we developed a model with five parameters to model the dispersive wave packet and obtained the parameter vector of each wave packet by the expectation-maximization (em) algorithm. The parameters in the model present the characteristics of signals, which can be further applied to locate and evaluate the structure's damage. To study the convergence property, synthetic signals with different sampling rates and noise intensities were considered. Furthermore the developed approach is also verified by the experimental data from an isotropic aluminum plate. (C) 2018 Elsevier Ltd. All rights reserved.
We introduce a novel probability density function for modeling the distribution of points around an ellipsoidal surface. This density is part of the family of elliptical distributions. We establish the theoretical con...
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We introduce a novel probability density function for modeling the distribution of points around an ellipsoidal surface. This density is part of the family of elliptical distributions. We establish the theoretical convergence properties of the parameter estimators and validate them using simulated data. Furthermore, we propose a mixture model utilizing this density, and we estimate its parameters using the Expectation-Maximization (em) algorithm. To assess its performance, we compare the algorithm to a state-of-the-art ellipse fitting method and conduct experiments on 3D real data obtained from depth cameras.
We consider the FASST framework for audio source separation, which models the sources by full-rank spatial covariance matrices and multilevel nonnegative matrix factorization (NMF) spectra. The computational cost of t...
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ISBN:
(纸本)9781479936878
We consider the FASST framework for audio source separation, which models the sources by full-rank spatial covariance matrices and multilevel nonnegative matrix factorization (NMF) spectra. The computational cost of the expectation-maximization (em) algorithm in [1] greatly increases with the number of channels. We present alternative em updates using discrete hidden variables which exhibit a smaller cost. We evaluate the results on mixtures of speech and real-world environmental noise taken from our DemAND database. The proposed algorithm is several orders of magnitude faster and it provides better separation quality for two-channel mixtures in low input signal-to-noise ratio (iSNR) conditions.
In this article, we develop a general statistical inference procedure for the probability of successful startup p in the case of startup demonstration tests when only the number of trials until termination of the expe...
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In this article, we develop a general statistical inference procedure for the probability of successful startup p in the case of startup demonstration tests when only the number of trials until termination of the experiment are observed. In particular, we define a class of startup demonstration tests and present expectation-maximization (em) algorithm to get the maximum likelihood estimate of p for this class. Most of well-known startup testing procedures are involved in this class. Extension of the results to Markovian startups is also presented.
A Markov-modulated independent sojourn process is a population process in which individuals arrive according to a Poisson process with Markov-modulated arrival rate, and leave the system after an exponentially distrib...
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A Markov-modulated independent sojourn process is a population process in which individuals arrive according to a Poisson process with Markov-modulated arrival rate, and leave the system after an exponentially distributed time. A procedure is developed to estimate the parameters of such a system, including those related to the modulation. It is assumed that the number of individuals in the system is observed at equidistant time points only, whereas the modulating Markov chain cannot be observed at all. An algorithm is set up for finding maximum likelihood estimates, based on the em algorithm and containing a forward-backward procedure for computing the conditional expectations. To illustrate the performance of the algorithm the results of an extensive simulation study are presented. (C) 2019 The Authors. Published by Elsevier B.V.
This paper presents a nonlinear state space model with considering a first-order autoregressive model for measurement noises. A recursive method using Taylor series based approximations for filtering, prediction and s...
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This paper presents a nonlinear state space model with considering a first-order autoregressive model for measurement noises. A recursive method using Taylor series based approximations for filtering, prediction and smoothing problem of hidden states from the noisy observations is designed. Also, an expectation-maximization algorithm for calculating the maximum likelihood estimators of parameters is presented. The closed form solutions are obtained for estimating of the hidden states and the unknown parameters. Finally, the performance of the designed methods are verified in a simulation study.
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