State-space models are ubiquitous in the statistical literature since they provide a flexible and interpretable framework for analyzing many time series. In most practical applications, the state-space model is specif...
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State-space models are ubiquitous in the statistical literature since they provide a flexible and interpretable framework for analyzing many time series. In most practical applications, the state-space model is specified through a parametric model. However, the specification of such a parametric model may require an important modeling effort or may lead to models which are not flexible enough to reproduce all the complexity of the phenomenon of interest. In such situations, an appealing alternative consists in inferring the state-space model directly from the data using a non-parametric framework. The recent developments of powerful simulation techniques have permitted to improve the statistical inference for parametric state-space models. It is proposed to combine two of these techniques, namely the Stochastic Expectation-Maximization (sem) algorithm and Sequential Monte Carlo (SMC) approaches, for non-parametric estimation in state-space models. The performance of the proposed algorithm is assessed though simulations on toy models and an application to environmental data is discussed. (C) 2020 Elsevier B.V. All rights reserved.
This paper addresses a study of target segmentation on two color images based on sem algorithm and region growing algorithm. Background image and target-existing image are converted from RGB space to HSV space. The Eu...
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ISBN:
(纸本)0780393953
This paper addresses a study of target segmentation on two color images based on sem algorithm and region growing algorithm. Background image and target-existing image are converted from RGB space to HSV space. The Euclid distance between these two transformed images in HSV space is computed and compared with that in RGB space. To segment the target region from the background, sem algorithm is applied. Then the MAP criterion is used for further segmentation. With certain prior knowledge about the size of the target, final segmentation result is got by region growing algorithm. The result of simulation shows that these segmentation methods are very efficient when used together.
In this paper, we discuss the parameter estimation for the generalized gamma distribution based on left-truncated and right-censored data. A stochastic version of the expectation-maximization (EM) algorithm is propose...
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In this paper, we discuss the parameter estimation for the generalized gamma distribution based on left-truncated and right-censored data. A stochastic version of the expectation-maximization (EM) algorithm is proposed as an alternative method to compute approximate maximum likelihood estimates. Two different methods to obtain reliable initial estimates of the parameters required for the iterative algorithms are also proposed. Interval estimation based on a parametric bootstrap method is discussed. The proposed methodologies are illustrated with a numerical example. Then, a Monte Carlo simulation study is used to evaluate the performance of the proposed estimation procedures and to compare with the direct optimization method and the conventional EM algorithm. Based on the simulation results, we show that the proposed stochastic EM algorithm is a useful alternative estimation method for the model fitting of the generalized gamma distribution.
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