Capturing the pattern of structural change is a relevant task in applied demand analysis, as consumer preferences may vary significantly over time. Filtering and smoothing techniques have recently played an increasing...
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Capturing the pattern of structural change is a relevant task in applied demand analysis, as consumer preferences may vary significantly over time. Filtering and smoothing techniques have recently played an increasingly relevant role. A dynamic Almost Ideal Demand System with random walk parameters is estimated in order to detect modifications in consumer habits and preferences, as well as changes in the behavioural response to prices and income. Systemwise estimation, consistent with the underlying constraints from economic theory, is achieved through the em algorithm. The proposed model is applied to UK aggregate consumption of alcohol and tobacco, using quarterly data from 1963 to 2003. Increased alcohol consumption is explained by a preference shift, addictive behaviour and a lower price elasticity. The dynamic and time-varying specification is consistent with the theoretical requirements imposed at each sample point. (c) 2005 Elsevier B.V. All rights reserved.
Traditional multilevel model assumed independence between groups. Datasets are different from traditional hierarchical data when it is grouped by geographical units. The individual is influenced by not only its region...
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ISBN:
(纸本)9781479921867
Traditional multilevel model assumed independence between groups. Datasets are different from traditional hierarchical data when it is grouped by geographical units. The individual is influenced by not only its region but also the adjacent regions. It could include spatial dependence between groups. Therefore, it is necessary to build a new model and estimation method. In this paper, spatial statistics and spatial econometric models are introduced to random intercept model. Spatial dependence is reflected by spatial lag model in traditional level-2 model. Four types of parameters which include fixed effects, random level-1 coefficients, variance-covariance components, and spatial correlation error parameter need to estimate. Maximum likelihood estimation based on em algorithm and Fisher scoring algorithm for improved random intercept model is employed.
The aim of this study is to find the maximum likelihood estimate (MLE) among frequency count data by using the expectation-maximization (em) algorithm in which is useful to impute the missing or hidden values. Two for...
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The aim of this study is to find the maximum likelihood estimate (MLE) among frequency count data by using the expectation-maximization (em) algorithm in which is useful to impute the missing or hidden values. Two forms of missing count data in both zero truncation and right censoring situations are illustrated for estimating the population size on drug use. The results show that a truncated and censored Poisson likelihood performs well with good estimates corresponding to the em algorithm with a numerically stable convergence, a monotone increasing likelihood, and providing local maxima, so the expected global maximum of the MLE depends on the initial value. (C) 2016 The Authors. Published by Elsevier B.V.
It is well-known that the em algorithm generally converges to a local maximum likelihood estimate. However, there have been many evidences to show that the em algorithm can converge correctly to the true parameters as...
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It is well-known that the em algorithm generally converges to a local maximum likelihood estimate. However, there have been many evidences to show that the em algorithm can converge correctly to the true parameters as long as the overlap of Gaussians in the sample data is small enough. This paper studies this correct convergence problem asymptotically on the em algorithm for Gaussian mixtures. It has been proved that the em algorithm becomes a contraction mapping of the parameters within a neighborhood of the consistent solution of the maximum likelihood when the measure of average overlap among Gaussians in the original mixture is small enough and the number of samples is large enough. That is, if the initial parameters are set within the neighborhood, the em algorithm will always converge to the consistent solution, i.e., the expected result. Moreover, the simulation results further demonstrate that this correct convergence neighborhood becomes larger as the average overlap becomes smaller. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
We consider the problem of estimation of the parameters of the Marshall-Olkin Bivariate Weibull distribution in the presence of random censoring. Since the maximum likelihood estimators of the parameters cannot be exp...
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We consider the problem of estimation of the parameters of the Marshall-Olkin Bivariate Weibull distribution in the presence of random censoring. Since the maximum likelihood estimators of the parameters cannot be expressed in a closed form, we suggest an em algorithm to compute the same. Extensive simulations are carried out to conclude that the estimators perform efficiently under random censoring. (C) 2010 Elsevier B.V. All rights reserved.
Mixture models implemented via the expectation-maximization (em) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the em algorithm requires ...
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Mixture models implemented via the expectation-maximization (em) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the em algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the em algorithm could improve its rate of convergence. In this paper, we show how this modified em algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the em algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
em algorithm is used to determine the maximum likelihood estimates when the data are progressively Type II censored, The method is shown to be feasible and easy to implement, The asymptotic variances and covariances o...
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em algorithm is used to determine the maximum likelihood estimates when the data are progressively Type II censored, The method is shown to be feasible and easy to implement, The asymptotic variances and covariances of the ML estimates are computed by means of the missing information principle. The methodology is illustrated with two popular models in lifetime analysis, the lognormal and Weibull lifetime distributions. (C) 2002 Elsevier Science B.V. All rights reserved.
作者：
Mino, HWashington Univ
Electron Syst & Signals Res Lab St Louis MO 63130 USA Toho Univ
Dept Informat Sci Chiba 2748510 Japan
This paper presents a method of estimating the parameters of intensity processes in the self-exciting point process (SEPP) with the expectation-maximization (em) algorithm. In the present paper, the case is considered...
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This paper presents a method of estimating the parameters of intensity processes in the self-exciting point process (SEPP) with the expectation-maximization (em) algorithm. In the present paper, the case is considered where the intensity process of SEPPs is dependent only on the latest occurrence, i,e,, one-memory SEPPs, as well as where the impulse response function characterizing the intensity process is parameterized as a single exponential function having a constant coefficient that fakes a positive or negative value, i.e., making it possible to model a self "-exciting" or "-inhibiting" point process. Then, an explicit formula is derived for estimating the parameters specifying the intensity process on the basis of the em algorithm, which in this instance gives the maximum likelihood (ML) estimates without solving nonlinear optimization problems In practical computations, the parameters of interest can he estimated from the histogram of time intervals between point events, Monte Carlo simulations illustrate the validity of the derived estimation formulas and procedures.
This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is to say, it uses only the edge or connectivity structure of the graph and does not draw on node or edge a...
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This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is to say, it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. We make two contributions. Commencing from: a probability distribution for matching errors, we show how the problem of graph matching can be posed as maximum-likelihood estimation using the apparatus of the em algorithm. Our second contribution is to cast the recovery of correspondence matches, between the graph nodes in a matrix framework. This allows us to efficiently recover correspondence matches, using singular value decomposition. We experiment with the method on both real-world and synthetic data. Here, we demonstrate that the method offers comparable performance to more computationally demanding methods.
In this paper, we address the problem of blindly separating convolutive mixtures of spatially and temporally independent sources. Source densities are modelled as mixtures of Gaussians. We present an em algorithm to c...
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In this paper, we address the problem of blindly separating convolutive mixtures of spatially and temporally independent sources. Source densities are modelled as mixtures of Gaussians. We present an em algorithm to compute Maximum Likelihood estimates of both the separating filters and the source density parameters, whereas in the state-of-the-art separating filters are usually estimated with gradient descent techniques. The use of the em algorithm, as an alternative to the usual gradient descent techniques, is advantageous as it provides a faster and more stable convergence and as it does not require the empirical tuning of a learning rate. Besides, we show how multichannel autoregressive spectral estimation techniques can be used in order to properly initialize the em algorithm. We demonstrate the efficiency of our em algorithm together with the proposed initialization scheme by reporting on simulations with artificial mixtures. Finally, we discuss the theoretical and practical limitations of our em approach and point out possible ways of addressing these issues in future works. (C) 2002 Elsevier Science B.V. All rights reserved.
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