Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 131 for “"EM Algorithm"”.
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An EM algorithm for Lidar deconvolution
… return signal. The expectation-maximization (EM) algorithm has been used in signal deconvolution, to produce a maximum-likelihood estimate (MLE) for the original signal. We explain the benefits of the EM algorithm over other benchmark algorithms in Lidar deconvolution, then propose a modified …
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Application of the EM Algorithm for Mixture Models
… by using the Expectation-Maximization (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as well as two other EM-based …
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Computation of Weights for Probabilistic Record Linkage Using the EM Algorithm
… time investment. In 1989, Matthew A. Jaro demonstrated how the Expectation-Maximization, or EM, algorithm could be used to compute the needed weights when fields have Binomial matching possibilities. This project applies this method of using the EM algorithm to calculate weights for …
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Contributions to Robust Methods: Modified Rank Covariance Matrix and Spatial-EM Algorithm
… assumption. They are, however, extremely sensitive to outlying observations, susceptible to small perturbation in data and poor in the efficiency for heavy-tailed distributions. A straightforward treatment is to replace the sample covariance matrix with a robust one. Visuri et al. …
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Implementation and evaluation of a dual-sensor time-adaptive EM algorithm for signal enhancement
This thesis describes the implementation and evaluation of an adaptive time-domain algorithm for signal enhancement from multiple-sensor observations. The algorithm is first derived as a noncausal time-domain algorithm, then converted into a causal, recursive form. A more computationally efficient …
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Analysis on the use of EM algorithm for space time coding in fading channels
… of space time trellis code is analyzed for the EM algorithm receiver. Using the proposed method of partial detected frame in slow, flat fading channel, it is found that the use of partial frame to estimate the mean is able to improve the EM algorithm further. In the study, the use of a minimum 2 …
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A new REML (PX)EM algorithm for linear mixed models and factor analytic mixed models
… these models is residual maximum likelihood (REML). Most statistical software packages available for the REML estimation of parameters associated with linear mixed models and factor analytic mixed models implement a Newton-Raphson type algorithm such as the expected information algorithm or the …
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A new REML (PX)EM algorithm for linear mixed models and factor analytic mixed models
… these models is residual maximum likelihood (REML). Most statistical software packages available for the REML estimation of parameters associated with linear mixed models and factor analytic mixed models implement a Newton-Raphson type algorithm such as the expected information algorithm or the …
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The estimation of missing values in hydrological records using the EM algorithm and regression methods
… of variable selection in regression. Here the emphasis is on finding efficient methods to identify the set of control stations which are likely to yield the best regression estimates of the missing values in the target station. The second class of methods is based on the EM algorithm, proposed …
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Dichotomous and polytomous item response model estimation using the marginal maximum likelihood and the EM algorithm
Item Response Theory is a set oflatent variable techniques specifically designed to model the interaction between a respondent's latent trait/ability and the test items' characteristics such as difficulties, discrimination powers and guessing liabilities. The Item Response Theory framework …
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Using the EM Algorithm to Estimate the Difference in Dependent Proportions in a 2 x 2 Table with Missing Data.
… are missing data. The Expectation-Maximization (EM) algorithm is used to obtain an estimate for the difference between correlated proportions. To obtain the standard error of this difference I employ a resampling technique known as bootstrapping. The performance of the bootstrap standard error is …
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Statistical algorithms using multisets and statistical inference of heterogeneous networks
… physics. In this thesis, we study three problems: improvement of the efficiency for the EM algorithm and the MCMC method, and statistical analysis for heterogeneous networks. The expectation-maximization (EM) algorithm is widely used in computing the maximum likelihood estimates when the …
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Accelerated clustering through locality-sensitive hashing
We obtain improved running times for two algorithms for clustering data: the expectation-maximization (EM) algorithm and Lloyd's algorithm. The EM algorithm is a heuristic for finding a mixture of k normal distributions in Rd that maximizes the probability of drawing n given data points. Lloyd's …
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Applications of computational statistics in cognitive diagnosis and IRT modeling
… likelihood estimation approach and utilizes the EM algorithm. It differs from other calibration procedures for IRT models such as BILOG in that we use Genetic Algorithm in the maximization (M) Step of the EM algorithm. Procedures for classifying examinees are also proposed. A simulation study …
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Parameter Estimation for Normally Distributed Grouped Data and Clustering Single-Cell RNA Sequencing Data via the Expectation-Maximization Algorithm
The Expectation-Maximization (EM) algorithm is an iterative algorithm for finding the maximum likelihood estimates in problems involving missing data or latent variables. The EM algorithm can be applied to problems consisting of evidently incomplete data or missingness situations, such as truncated …
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Multigrid Algorithms for Massively Parallel Machines
… solutions of partial differential equations (<em>pde's</em>) are required in many physical problems arising in areas such as computational fluid dynamics, atmospheric sciences, electromagnetics etc. One of the most popular methods of solving <em>pde's</em> is the use of the multigrid algorithm. …
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Planar detection using modified expectation maximization
… an image pair is cast as an incomplete data problem where the parameters to be estimated are the ones that define the homographies induced by the planar regions in the scene. This incomplete data problem motivates the employment of the Expectation Maximization (EM) algorithm. Derivation of the EM …
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New Multivariate Zero-Inflated Beta-Binomial Distribution
… parameters of the MZIBB model via Newton-Raphson algorithm, Fisher scoring algorithm, and EM algorithm. The score test and likelihood ratio test are derived for testing the significance of zero-inflation parameter ω. The performance of the EM algorithm is evaluated by giving different group …
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Bayesian Approach Dealing with Mixture Model Problems
… many applications. An Expectation Maximization (EM) algorithm and Markov Chain Monte Carlo (MCMC) are two popular methods to estimate parameters in a finite mixture model. However, both of the methods may converge to local maximum regions rather than the global maximum when multiple local maxima …
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Parameter estimation in HMMs with guaranteed convergence
The EM (Expectation-Maximization) algorithm is a heuristic for parameter estimation in statistical models with latent variables, where explicit computation of the maximum likelihood estimate (MLE) is infeasible. Although widely used in practice, the theoretical guarantees associated with EM are …
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