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 32 for “"Maximum Likelihood Estimate"”.
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A maximum likelihood estimate of dizygotic cotwin genetic similarity: a genetic analysis of quantitative traits
This document only includes an excerpt of the corresponding thesis or dissertation. To request a digital scan of the full text, please contact the Ruth Lilly Medical Library's Interlibrary Loan Department (rlmlill@iu.edu).
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Passive localization of underwater acoustic beacons
… multipliers are introduced to solve for the maximum likelihood estimate of the acoustic beacon's position. An iterative algorithm is developed using range difference measurements to solve for the maximum likelihood estimate of a stationary acoustic beacon's position. This algorithm is then …
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Estimation For Simple Linear Regression With Exponentially Distributed Errors
… with scale parameter ϴ. We derive both the maximum likelihood estimate and the least square estimate and examine their important properties.
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Clustering Analysis of Zernike Coefficients From High Order Aberration Patients
… models (CLM). EM algorithm is used to infer the maximum likelihood estimate of parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters. The Bootstrap method is used to estimate the …
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Tomographic measurements of barotropic motions
… coast of Northern California (4000 km range). A maximum likelihood estimate of the change in acoustic travel time (based on phase) between received pulses is used to estimate barotropic fluctilations. Analysis of the resulting time series reveals resonant oscillations at nontidal frequencies in …
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Adaptive Control and Parameter Estimation in Markov Chains: A Quadratic Case
… α. Particularly, we study the behavior of the maximum likelihood estimate of α at each time n as n increases under an arbitrary realizable control. We show that the results of [8] extend to the quadratic case with a few additional assumptions. These results are that the sequence of estimates of …
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An EM algorithm for Lidar deconvolution
… 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 EM algorithm with smoothing and denoising parameters to address …
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Inference for the survival function of the linearly decreasing stress Weibull
… appears in Guenther (2014). Parameters will be estimated using maximum likelihood estimation. The approximate normality of the maximum likelihood estimate of the survival function will be studied through a small scale simulation study. Various methods for finding confidence intervals of the …
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Parameter estimation in HMMs with guaranteed convergence
… variables, where explicit computation of the maximum likelihood estimate (MLE) is infeasible. Although widely used in practice, the theoretical guarantees associated with EM are quite weak. We study the setting of a hidden Markov model (HMM) with two hidden states, where the (symmetric) …
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Mixture Modeling for Multivariate Observations
… the application of these mixture models to estimate nonparametrically a multivariate distribution. The major difficulty with the likelihood approach that is associated with the estimation of these mixtures in the multivariate case is that the likelihood function cannot be maximized directly …
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Divide and recombine for large complex data: The subset likelihood modeling approach to recombination
… method. Here we study D&R methods for likelihood-based model fitting. We introduce a notion of likelihood analysis and modeling. We divide the data and fit a likelihood model on each subset. The fitted model is characterized by a set of parameters much smaller than the subset data size, …
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Canonical Correlation Analysis for Longitudinal Data
… an iterative algorithm to determine the maximum likelihood estimate of the Kronecker product covariance structure for one set of variables. We implement and generalize their method to estimate the covariance parameters in the context of canonical correlation analysis. We implemented …
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Sensor Array Processing with Manifold Uncertainty
… the wavefield at discrete locations in space, an estimate of the spatial spectrum can be derived using basic wave propagation models. The observable data space corresponding to physically realizable source locations for a given array configuration is referred to as the array manifold. In this …
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Shape-Constrained Inference for Concave-Transformed Densities and their Modes
We consider inference about functions estimated via shape constraints based on concavity. We consider log-concave densities and other “concave-transformed” densities on the real line, where a concave-transformed class is one given by applying a transformation (e.g. the logarithm or a power …
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An extended Kalman filter extension of the augmented Markov decision process
… though, the robot's position can only be estimated using incomplete and imperfect information from its sensors and an approximate model of its dynamics. Algorithms which assume perfect knowledge of the robot's position can still be applied by treating the mean or maximum likelihood …
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Robust mixtures of regression models
… assume a normal distribution for error and then estimate the regression param- eters by the maximum likelihood estimate (MLE). In this project, we demonstrate that the MLE, like the least squares estimate, is sensitive to outliers and heavy-tailed error distributions. We propose a robust …
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Magnitude, concentration, and metric complexity in phylogenetics and information theory
… models and problems that arise when one wants to estimate evolutionary parameters from large biological datasets. The classical setting for inference assumes that the number of independent sites, k, is much larger than the number of leaves, n, on a phylogenetic tree. In Chapter 2, we add to the …
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Modeling Strategies for Large Dimensional Vector Autoregressions
… observations and give the analytical form of its maximum likelihood estimate. Then we describe how to integrate the proposed reduced-rank estimator into the fitting of large dimensional VAR models, where we consider two scenarios that require different model fitting procedures. In the VAR modeling …
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Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation-maximization under non-Bayesian and Bayesian framework
… input model parameter uncertainty using the Maximum Likelihood Estimate (MLE) and Maximum a Posteriori (MAP) estimate. The difference between experimental measurements and nominal code predictions, which are the observables, are considered scalar random variables. A dispersed normal prior is …
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An integrated performance model learning and planning approach for optimal infrastructure facility maintenance under partial observability
… agency-facility interaction is used to learn the maximum likelihood estimate of performance model using the Baum-Welch algorithm. Both offline and online versions of the learning algorithm are presented. The probing-optimizing dichotomy, also known as exploration-exploitation dilemma, in choosing …
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