The classical em algorithm for the restoration of the mixture of normal probability distributions cannot determine the number of components in the mixture. An algorithm called ARD em for the automatic determination of...
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The classical em algorithm for the restoration of the mixture of normal probability distributions cannot determine the number of components in the mixture. An algorithm called ARD em for the automatic determination of the number of components is proposed, which is based on the relevance vector machine. The idea behind this algorithm is to use a redundant number of mixture components at the first stage and then determine the relevant components by maximizing the evidence. Experiments with model problems show that the number of clusters thus determined either coincides with the actual number or slightly exceeds it. In addition, clusterization using ARD em turns out to be closer to the actual clusterization than that obtained by the analogs based on cross validation and the minimum description length principle.
The expectation-maximization (em) algorithm isa powerful computational technique for locating maxima of functions. It is widely used in statistics for maximum likelihood or maximum a posteriori estimation in incomplet...
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The expectation-maximization (em) algorithm isa powerful computational technique for locating maxima of functions. It is widely used in statistics for maximum likelihood or maximum a posteriori estimation in incomplete data models. In certain situations, however, this method is not applicable because the expectation step cannot be performed in closed form. To deal with these problems, a novel method is introduced, the stochastic approximation em (SAem), which replaces the expectation step of the em algorithm by one iteration of a stochastic approximation procedure. The convergence of the SAem algorithm is established under conditions that are applicable to many practical situations. Moreover,it is proved that, under mild additional conditions, the attractive stationary points of the SAem algorithm correspond to the local maxima of the function. presented to support our findings.
In epiderniological Studies Where Subjects are seen periodically on follow-up visits, interval-censored data Occur naturally. The exact time the change of state (such as HIV seroconversion) Occurs is not known exactly...
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In epiderniological Studies Where Subjects are seen periodically on follow-up visits, interval-censored data Occur naturally. The exact time the change of state (such as HIV seroconversion) Occurs is not known exactly, only that it Occurred sometime within a specific time interval. Methods of estimation for interval-censored data are readily available when data are independent. However, methods for correlated interval-censored data are not well developed. This paper considers an approach for estimating the parameters when data are interval-censored and correlated within Sexual partnerships. We consider the exact event times for interval-censored observations as unobserved data, only known to be between two time points. Dependency induced by Sexual partnerships is modelled as frailties assuming a gamma distribution for frailties and an exponential distribution on the time to infection. This formulation facilitates application of the expectation-maximization (em) algorithm. Maximization process maximizes the standard Survival frailty model. Results Show high degree of heterogeneity between sexual partnerships. Intervention strategies aimed at combating the spread of HIV and other sexually transmitted infections (STI)s Should treat sexual partnerships as social units and fully incorporate the effects of migration in their strategies. Copyright (c) 2006 John Wiley & Sons, Ltd.
Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which researchers hope to predict (local) therapeutic effi...
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Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual's response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying pathological/physiological changes via qMRI, due to measurement errors of unpredictable influences. We use a hidden Markov radom field to model the spatial dependence in the data and develop a maximum likelihood approach via the Expectation-Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. More importantly, we focus on the expected changes rather than image segmentation. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations.
In this article we develop an estimation method based on the augmented data scheme and em/Sem (Stochastic em) algorithms for fitting one-parameter probit (Rasch) IRT (Item Response Theory) models. Instead of using the...
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In this article we develop an estimation method based on the augmented data scheme and em/Sem (Stochastic em) algorithms for fitting one-parameter probit (Rasch) IRT (Item Response Theory) models. Instead of using the S steps of the Sem algorithm, that is, instead of simulating values for the unobserved variables (augmented data and the latent traits), we consider the conditional expectations of a set of unobserved variables on the other set of unobserved variables, the current estimates of the parameters and the observed data, based on the full conditional distributions from the Gibbs sampling algorithm. Our method, named the CADem algorithm (conditional augmented data em), presents straightforward E steps, which avoid the need to evaluate the usual integrals, also facilitating the M steps, without the need to use numerical methods of optimization. We use the CADem algorithm to obtain both maximum likelihood estimates and maximum a posteriori estimates of the difficulty parameters for the one-parameter probit (Rasch) model. Also, we obtain estimates for the latent traits, based on conditional expectations. In addition, we show how to calculate the associated standard errors. Some directions are provided to extend our approach to other IRT models. In this respect, we perform a simulation study to compare the estimation methods. The results indicated that our approach is quite comparable to the usual marginal maximum likelihood (MML) and Gibbs sampling methods (GS) in terms of parameter recovery. However, CADem is as fast as MML and as flexible as GS.
We propose a new estimator of the probability density function when the data is randomly censored, obtained through an em algorithm, for solving a "nonlinearly smoothed" maximum likelihood problem. This algo...
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We propose a new estimator of the probability density function when the data is randomly censored, obtained through an em algorithm, for solving a "nonlinearly smoothed" maximum likelihood problem. This algorithm is based on setting the Gateaux derivatives, in all directions, of the smoothed likelihood function equal to zero. Simulation results are presented which suggest that the proposed estimator compares favorably to the Kaplan-Meier-based kernel density estimator.
In this paper, we consider the four-parameter bivariate generalized exponential distribution proposed by Kundu and Gupta [Bivariate generalized exponential distribution, J. Multivariate Anal. 100 (2009), pp. 581-593] ...
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In this paper, we consider the four-parameter bivariate generalized exponential distribution proposed by Kundu and Gupta [Bivariate generalized exponential distribution, J. Multivariate Anal. 100 (2009), pp. 581-593] and propose an expectation-maximization algorithm to find the maximum-likelihood estimators of the four parameters under random left censoring. A numerical experiment is carried out to discuss the properties of the estimators obtained iteratively.
Stochastic volatility (SV) models have become increasingly popular for explaining the behaviour of financial variables such as stock prices and exchange rates, and their popularity has resulted in several different pr...
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Stochastic volatility (SV) models have become increasingly popular for explaining the behaviour of financial variables such as stock prices and exchange rates, and their popularity has resulted in several different proposed approaches to estimating the parameters of the model. An important feature of financial data, which is commonly ignored, is the occurrence of irregular sampling because of holidays or unexpected events. We present a method that can handle the estimation problem of SV models when the sampling is somewhat irregular. The basic idea of our approach is to combine the expectation-maximization (em) algorithm with particle filters and smoothers in order to estimate parameters of the model. In addition, we expand the scope of application of SV models by adopting a normal mixture, with unknown parameters, for the observational error term rather than assuming a log-chi-squared distribution. We address the problems by using state-space models and imputation. Finally, we present simulation studies and real data analyses to establish the viability of the proposed method.
作者：
Li, QianZhengde Polytech
Dept Elect & Informat Technol Nanjing 211106 Jiangsu Peoples R China
The abnormality of communication link of mobile Internet of Things will threaten the security of communication of mobile Internet of Things, and the existing abnormality detection method is limited due to low accuracy...
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The abnormality of communication link of mobile Internet of Things will threaten the security of communication of mobile Internet of Things, and the existing abnormality detection method is limited due to low accuracy, long time consumption and high energy consumption. To this end, the anomaly detection method of communication link of mobile Internet of Things based on em algorithm is proposed in this study. Firstly, the anomaly range of the Internet of Things is located according to the communication node information of the data changes. Then the abnormal link of the target is judged and the anomaly feature of the communication link of the Internet of Things based on twin neural network is extracted. Finally, em algorithm is improved with semi-supervised machine learning method to detect abnormal communication links of mobile Internet of Things. The experimental results show that the proposed method has the advantages of high precision, short time consumption and low energy consumption in the anomaly detection of communication links in the Internet of Things.
The em algorithm is widely used to calculate maximum-likelihood estimates corresponding to a mixture of normal distributions. The algorithm is altered using simple constraints which increase robustness against poor in...
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The em algorithm is widely used to calculate maximum-likelihood estimates corresponding to a mixture of normal distributions. The algorithm is altered using simple constraints which increase robustness against poor initial guesses while maintaining a low work requirement per iteration. The modified algorithm is described and the results of some numerical tests are given.
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