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 27 for “"multivariate Gaussian"”.
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Semiparametric estimation with clustered right censored data via multivariate gaussian random fields
… censored data is probit-transformed yielding a multivariate Gaussian random field preserving the spatial correlation function. The data is analyzed using counting process and geostatistical formulation that led to a class of weighted pairwise semiparametric estimating functions. In the …
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Microbubble localization using multivariate gaussian fitting for super-resolution ultrasound localization microscopy
… localization microscopy. In this study, the multivariate Gaussian fitting (MGF) method is applied to achieve localization of spatially overlapping microbubble signals. The MGF-based localization technique provides a customizable size and shape for both isolated and overlapping microbubbles. …
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First order bias and second order variance of the Maximum Likelihood Estimator with application to multivariate Gaussian data and time delay and Doppler shift estimation
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 2000.
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Development of an Exteroceptive Sensor Suite on Unmanned Surface Vessels for Real-Time Classification of Navigational Markers
… the LIDAR data to be classified using either Multivariate Gaussian or Parzen Window Classifiers. Both produce 96% accuracy or better, however, multivariate Gaussian ran considerably faster than the Parzen and was simpler to implement and was therefore chosen as the final classifier. …
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Statistical Methods for Multi-type Recurrent Event Data Based on Monte Carlo EM Algorithms and Copula Frailties
… (MCEM) algorithm and copula functions for the multivariate variables are also presented in this chapter. Chapter 2 develops a multi-type recurrent events model with multivariate Gaussian random effects (frailties) for the intensity functions. In this chapter, we present nonparametric baseline …
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Statistical analysis of correlated fossil fuel securities
… The first method, forecasting using conditional multivariate Gaussian statistics, was shown to yield, in a relative sense, the best results for those securities which exhibited a high correlation with the rest of the basket. For the second method, principal component analysis was done on a basket …
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Bayesian Analysis of Temporal and Spatio-temporal Multivariate Environmental Data
… change has remained elusive. Moreover, modeling multivariate spatio-temporal data is computationally expensive. There is great need to computationally feasible models that account for temporal, spatial, and inter-variables dependence. Our research focuses on those areas in two ways. First, we …
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Dimensionality Reduction and Fusion Strategies for the Design of Parametric Signal Classifiers
… overcoming the dimensionality curse for multivariate classifier implementation but also provides a means to further select, out of a rank-ordered set, a smaller set of features that give the best classification accuracies. Because the class-conditional densities of transform feature …
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Three essays on econometrics: Network estimators with applications and assessment of the effects of Covid-19 pandemic
… The first paper focuses on random vectors with multivariate Gaussian distribution. In this specific case, a graph embedding the conditional dependencies can be obtained from the precision matrix, which is the inverse of the covariance matrix. Furthermore, by a proper re-scaling of this matrix, …
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Contribution au classement des fruits par analyse d'images numériques. Application au tri en ligne des pommes Golden delicious et Jonagold.
… uniform colour. This later was modelled by a multivariate Gaussian distribution and the defect detection was carried out by computing the Mahalanobis distance separating a pixel's colour and the mean colour of the fruit. For Jonagold apples, having a multimodal colour frequency distribution, …
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Visual-inertial odometry with depth sensing using a multi-state constraint Kalman filter
… feature observation by approximating it as a multivariate Gaussian distribution [11]. The extended MSCKF algorithm is presented and its performance is compared to the original MSCKF algorithm using real-world data obtained by flying a custom-built quadrotor in an indoor office environment.
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A new approach to calculate and forecast dynamic conditional correlation - the use of a multivariate heteroskedastic mixture model
… (conditional returns) can be modelled using multivariate Gaussian mixture distribution and multivariate T mixture distribution. A key motivation of proposing mixture models is to account for the bi-modality observed in unconditional distribution of realized correlation. Besides, the ultimate …
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Mass transfer and dispersion processes in connected conductivity structures : simulation, visualization, delineation and application
… heterogeneity, specifically that the field is multivariate gaussian. We move away from the multigaussian assumption to focus on the concept of connected pathways of high or low conductivity. We first motivate the importance of connected extreme conductivity values through the numerical creation …
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Composite Likelihood: Multiple Comparisons and Non-Standard Conditions in Hypothesis Testing
… intensity in using full likelihood estimation of multivariate and correlated data is a valid motivation to employ composite likelihood as an alternative that eases the process by using marginal or conditional densities and reducing the dimension. We study the problem of multiple hypothesis testing …
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Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
… parts. The second part discusses two population multivariate normal distributions with common covariance matrix. The goal for this part is to derive objective/non-informative priors for the parameterizations and use these priors to build up constructive random posteriors of the Kullback-Liebler …
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Combining Probabilistic Shape-from-Shading & Statistical Facial Shape Models
… combining the two types of surface normals using multivariate Gaussian distributions on the tangent plane. This chapter also details how the statistical surface height model is used to recover surface heights from surface normals. The Fisher criterion and smoothing are used to deal with outliers …
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Longitudinal analysis for binary and count data
… for each of many subjects. In many fields, multivariate analysis-of-variance is commonly used to analyse longitudinal data. Such an analysis is appropriate when responses for each subject are multivariate Gaussian with a common covariance matrix for all subjects. In many cases, however, the …
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Optimization Methods for Machine Learning under Structural Constraints
… approaches. In the third chapter, we focus on Gaussian Graphical Models, which aims to estimate a sparse precision matrix from iid multivariate Gaussian samples. We propose a novel estimator via ℓ₀ℓ₂-penalized pseudolikelihood. We then design a specialized nonlinear Branch-and-Bound (BnB) …
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Device-type Profiling using Packet Inter-Arrival Time for Network Access Control
… namely K-means clustering, clustering-based multivariate gaussian outlier score, and long short-term memory networks algorithms. These algorithms are capable of identifying abnormal inter-arrival time patterns based on device-types. The effectiveness of the proposed technique is evaluated …
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Multivariate Models and Algorithms for Systems Biology
… parts. Inthe first part, we present a suite of multivariate approaches for a reliable discovery of geneclusters, often interpreted as pathway components, from molecular profiling data with replicated measurements. We translate our goal into learning an optimal correlation structure from …
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