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15th International Conference on Systems, Signals and Image Processing

作者： Horta, Michelle M. Mascarenhas, Nelson D. A. Frery, Alejandro C. Levada, Alexandre L. M. Univ Sao Paulo Phys Inst Sao Carlos Trabalhador Sao Carlense Ave 400Postal Code 369 BR-13560970 Sao Carlos SP Brazil Univ Fed Sao Carlos Dept Comp BR-13565905 Sao Carlos SP Brazil Univ Fed Alagoas Comp Inst BR-57072970 Maceio AL Brazil

ISBN: (纸本)9788022728560

This paper presents a novel method for clustering multilook polarimetric SAR images by combining the stochastic expectation-maximization (sem) algorithm with the mixture of G(p)(0) distributions, using the method of moments for parameter estimation. The pixel values of multilook SAR data are complex covariance matrices, and they are described by mixtures of G(p)(0) laws. This distribution can describe different type of targets;like urban areas, forest and pasture. The proposed clustering technique can be applied to unsupervised classification and segmentation process. Experiments with real image data provide good results. http://***/stamp/***?tp=&arnumber=4604372

关键词： multilook polarimetric SAR image clustering sem algorithm finite mixture model

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Parameter Estimation in a Hierarchical Random Intercept Model with Censored Response: An Approach using a sem algorithm and Gibbs Sampling

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SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS 2014年第2期76卷 210-233页

作者： Slaoui, Y. Nuel, G. Univ Poitiers Lab Math & Applicat 11 Blvd Marie & Pierre Curie F-86962 Poitiers France Univ Paris 05 Paris France

In this paper, we propose an approach, based on the stochastic expectation maximization (sem) algorithm and Gibbs sampling, to deal with the problem caused by censoring in the response of a hierarchical random intercept models. We compared our approach with the existing methods via real data sets as well as simulations. Results showed that our approach outperformed other approaches in terms of estimation accuracy and computing efficiency.

关键词： Gibbs sampling hierarchical random intercept models linear mixed effects models sem algorithm censored data truncated normal distribution

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On estimating parameters of the exponential-logarithmic distribution under progressively type-II censored data

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2025年

作者： Aghamohammadi, Seyedeh Zahra Islamic Azad Univ Dept Math Islamshahr Branch Islamshahr Iran

We investigate the problem of estimating unknown parameters of the Exponential-Logarithmic distribution under classical and Bayesian approaches when samples are observed based on progressively type-II censoring. Under classical approach, it is observed that the Maximum Likelihood Estimators (MLEs) do not have closed form expressions. We use both the EM and sem algorithms to compute the MLEs and using the missing information principle obtain the observed Fisher information matrix which is then used to construct the asymptotic confidence intervals. Under Bayesian approach, we obtain Bayes estimators with respect to different symmetric and asymmetric loss functions. In this regard, we use Tierney-Kadane and the importance sampling procedures. In practical setting, we analyze a real data set and conduct a Monte Carlo simulation study to examine the performance of the proposed estimators.

关键词： Bayesian estimation Exponential-logarithmic distribution Importance sampling Progressive type-II censoring sem algorithm

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How well do sem algorithms imitate EM algorithms? A non-asymptotic analysis for mixture models

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2020年第1期14卷 147-173页

作者： Bloemer, Johannes Brauer, Sascha Bujna, Kathrin Kuntze, Daniel Paderborn Univ Warburger Str 100 D-33098 Paderborn Germany SAP SE Walldorf Germany

In this paper, we present a theoretical and an experimental comparison of EM and sem algorithms for different mixture models. The sem algorithm is a stochastic variant of the EM algorithm. The qualitative intuition behind the sem algorithm is simple: If the number of observations is large enough, then we expect that an update step of the stochastic sem algorithm is similar to the corresponding update step of the deterministic EM algorithm. In this paper, we quantify this intuition. We show that with high probability the update equations of any EM-like algorithm and its stochastic variant are similar, given that the input set satisfies certain properties. For instance, this result applies to the well-known EM and sem algorithm for Gaussian mixture models and EM-like and sem-like heuristics for multivariate power exponential distributions. Our experiments confirm that our theoretical results also hold for a large number of successive update steps. Thereby we complement the known asymptotic results for the sem algorithm. We also show that, for multivariate Gaussian and multivariate Laplacian mixture models, an update step of sem runs nearly twice as fast as an EM update set.

关键词： EM algorithm sem algorithm Mixture models Non-asymptotic analysis

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An empirical comparison of EM, sem and MCMC performance for problematic Gaussian mixture likelihoods

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STATISTICS AND COMPUTING 2004年第4期14卷 323-332页

作者： Dias, JG Wedel, M ISCTE Dept Quantitat Methods P-1649026 Lisbon Portugal Univ Michigan Sch Business Ann Arbor MI 48109 USA

We compare EM, sem, and MCMC algorithms to estimate the parameters of the Gaussian mixture model. We focus on problems in estimation arising from the likelihood function having a sharp ridge or saddle points. We use both synthetic and empirical data with those features. The comparison includes Bayesian approaches with different prior specifications and various procedures to deal with label switching. Although the solutions provided by these stochastic algorithms are more often degenerate, we conclude that sem and MCMC may display faster convergence and improve the ability to locate the global maximum of the likelihood function.

关键词： Gaussian mixture models EM algorithm sem algorithm MCMC label switching loss functions conjugate prior hierarchical prior

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sem metrology on bit patterned media nanoimprint template: issues and improvements

SEM metrology on bit patterned media nanoimprint template: i...

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Conference on Metrology, Inspection, and Process Control for Microlithography XXVI

作者： Hwu, Justin J. Babin, Sergey Yushmanov, Peter Seagate Technol 47010 Kato Rd Fremont CA 94536 USA aBeam Technol Inc Castrro Valley CA 94546 USA

ISBN: (纸本)9780819489807

Critical dimension measurement is the most essential metrology needed in nanofabrication processes and the practice is most commonly executed using sems for its flexibility in sampling, imaging, and data processing. In bit patterned media process development, nanoimprint lithography (NIL) is used for template replication and media fabrication. sem imaging on templates provide not only individual dot size, but also information for dot size distribution, the location of dots, pitch and array alignment quality, etc. It is very important to know the sem measurement limit since the feature nominal size is less than 20 nm and the dot feature size and other metrics will relate to the final media performance. In our work an analytical sem was used. We performed and compared two imaging analysis approaches for metrology information. The sem beam was characterized using BEAMETR test sample and software for proper beam condition setup. A series of images obtained on a 27 nm nominal pitch dot array patterns were analyzed by conventional brightness intensity threshold method and physical model based analysis using myCD-CH software. Through comparison we identified the issues with threshold method and the strength of using model based analysis for its improvement in feature size and pitch measurement uncertainty and accuracy. TEM cross sections were performed as accuracy reference for better understanding the source of measurement accuracy deviation.

关键词： sem metrology sem algorithm sem precision sem accuracy beam spot size image analysis CD measurement

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Application of analytic sem to CD metrology at nanometer scale

Application of analytic SEM to CD metrology at nanometer sca...

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Conference on Metrology, Inspection, and Process Control for Microlithography XXIV

作者： Hwu, Justin J. Babin, Sergey Bay, Konstantin Seagate Technol 47010 Kato Rd Fremont CA 94536 USA aBeam Technol Castro Valley CA 94546 USA

ISBN: (纸本)9780819480521

sem metrology involves uncertainty of the linewidth measurement because the sem signal formation is an extremely complex process. In this work, we used an analytical sem for CD metrology applications on quartz nanoimprint template. The sem was tuned first to find the best reasonable condition for consistent operation. Beam characterization was done using BEAMETR beam measurement technique. sem images of templates were taken at optimum conditions. The measurements were done using a) regular imaging processing software and b) using physical model based processing tool myCD. The quartz template was then measured using TEM crossections at selected sites to reveal profile information as metrology comparison reference. The metrology capability and fundamental limitation of analytical sem operation with regular imaging processing was identified. Information about sem setup and materials was used. The considerable improvement using the physical modeling imaging process was found.

关键词： sem metrology sem algorithm beam spot size Monte Carlo simulation

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An Akaike information criterion for model selection in the presence of incomplete data

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 1998年第1期67卷 45-65页

作者： Cavanaugh, JE Shumway, RH Univ Missouri Dept Stat Columbia MO 65211 USA Univ Calif Davis Div Stat Livermore CA 95616 USA

We derive and investigate a variant of AIC, the Akaike information criterion, for model selection in settings where the observed data is incomplete. Our variant is based on the motivation provided for the PDIO ('predictive divergence for incomplete observation models') criterion of Shimodaira (1994, in: Selecting Models from Data: Artificial Intelligence and Statistics IV, Lecture Notes in Statistics, vol. 89, Springer, New York, pp. 21-29). However, our variant differs from PDIO in its 'goodness-of-fit' term. Unlike AIC and PDIO, which require the computation of the observed-data empirical log-likelihood, our criterion can be evaluated using only complete-data tools, readily available through the EM algorithm and the sem ('supplemented' EM) algorithm of Meng and Rubin (Journal of the American Statistical Association 86 (1991) 899-909). We compare the performance of our AIC variant to that of both AIC and PDIO in simulations where the data being modeled contains missing values. The results indicate that our criterion is less prone to overfitting than AIC and less prone to underfitting than PDIO. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： AIC EM algorithm information theory Kullback-Leibler information model selection criteria PDIO criterion sem algorithm

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Discriminating among inverse Weibull, lognormal, and inverse Gaussian distributions

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第4期40卷 1698-1718页

作者： Diyali, Bishal Kumar, Devendra Singh, Sukhdev Cent Univ Haryana Dept Stat Jaat Haryana India Thapar Inst Engn & Technol Sch Math Patiala Punjab India

Inverse Weibull, lognormal, and inverse Gaussian are some commonly used statistical distributions for modeling positively skewed failure lifetime data. These distributions share some interesting properties among themselves like they all have uni-modal hazard rates. In this paper, we address the problem of discriminating among these statistical distributions to consider a more appropriate lifetime model. We first consider the complete samples and make use of the maximized log-likelihood approach for choosing the correct model. We also obtain the expressions for logarithmic of defined test statistics, and associated asymptotic distributions. We then extend our discussion based on the observed sample in the presence of some censoring. We perform a simulation study in both cases to compare the probabilities of correct selection. Furthermore, for a given probability of correct selection and user-specified protection level, we present a discussion to determine the minimum sample size required to discriminate among the three lifetime models. Finally, two real data sets are analyzed to illustrate the proposed methodology. In our findings, we observed that model parameters and methods of estimating unknown parameters play very important roles in the discrimination process, and sample size and the proportion of censoring become key factors to ensure a high probability of selecting a correct model.

关键词： asymptotic distributions likelihood ratio test model selection probability of correct selection sem algorithm

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A criterion for model selection in the presence of incomplete data based on Kullback's symmetric divergence

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SIGNAL PROCESSING 2005年第7期85卷 1405-1417页

作者： Seghouane, AK Bekara, M Fleury, G Ecole Super Elect Serv Mesures F-91192 Gif Sur Yvette France

A criterion is proposed for model selection in the presence of incomplete data. It's construction is based on the motivations provided for the KIC criterion that has been recently developed and for the PDIO (predictive divergence for incomplete observation models) criterion. The proposed criterion serves as an asymptotically unbiased estimator of the complete data Kullback-Leibler symmetric divergence between a candidate model and the generating model. It is therefore a natural extension of KIC to settings where the observed data is incomplete and is equivalent to KIC when there is no missing data. The proposed criterion differs from PDIO in its goodness of fit term and its complexity term, but it differs from AIC(cd) (where the notation "cd" stands for "complete data") only in its complexity term. Unlike AIC, KIC and PD10 this criterion can be evaluated using only complete data tools, readily available through the EM and sem algorithms. The performance of the proposed criterion relative to other well-known criteria are examined in a simulation study. (c) 2005 Elsevier B.V. All rights reserved.

关键词： model selection Kullback-Leibler information AIC(cd) criterion PDIO criterion KIC criterion EM algorithm sem algorithm

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