subject:(Random Matrix Model)

Texas A&M University

1. Liu, Wei. A New Asset Pricing Model based on the Zero-Beta CAPM: Theory and Evidence.

Degree: PhD, Finance, 2013, Texas A&M University

URL: http://hdl.handle.net/1969.1/149521

► This work utilizes zero-beta CAPM to derive an alternative form dubbed the ZCAPM. The ZCAPM posits that asset prices are a function of market risk… (more)

Subjects/Keywords: Asset Pricing; Zero-Beta CAPM; Factor Model; Random Matrix Theory

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APA (6^th Edition):

Liu, W. (2013). A New Asset Pricing Model based on the Zero-Beta CAPM: Theory and Evidence. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/149521

Chicago Manual of Style (16^th Edition):

Liu, Wei. “A New Asset Pricing Model based on the Zero-Beta CAPM: Theory and Evidence.” 2013. Doctoral Dissertation, Texas A&M University. Accessed January 24, 2021. http://hdl.handle.net/1969.1/149521.

MLA Handbook (7^th Edition):

Liu, Wei. “A New Asset Pricing Model based on the Zero-Beta CAPM: Theory and Evidence.” 2013. Web. 24 Jan 2021.

Vancouver:

Liu W. A New Asset Pricing Model based on the Zero-Beta CAPM: Theory and Evidence. [Internet] [Doctoral dissertation]. Texas A&M University; 2013. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/1969.1/149521.

Council of Science Editors:

Liu W. A New Asset Pricing Model based on the Zero-Beta CAPM: Theory and Evidence. [Doctoral Dissertation]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/149521

University of California – Berkeley

2. Dey, Partha Sarathi. Contributions to Stein's method and some limit theorems in probability.

Degree: Statistics, 2010, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/9qh9h989

► In this dissertation we investigate three different problems related to (1) concentration inequalities using Stein's method of exchangeable pair, (2) first-passage percolation along thin lattice… (more)

Subjects/Keywords: Statistics; Mathematics; Concentration inequality; Curie-Weiss model; First-passage percolation; Random graph; Random matrix; Stein's method

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APA (6^th Edition):

Dey, P. S. (2010). Contributions to Stein's method and some limit theorems in probability. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/9qh9h989

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Dey, Partha Sarathi. “Contributions to Stein's method and some limit theorems in probability.” 2010. Thesis, University of California – Berkeley. Accessed January 24, 2021. http://www.escholarship.org/uc/item/9qh9h989.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Dey, Partha Sarathi. “Contributions to Stein's method and some limit theorems in probability.” 2010. Web. 24 Jan 2021.

Vancouver:

Dey PS. Contributions to Stein's method and some limit theorems in probability. [Internet] [Thesis]. University of California – Berkeley; 2010. [cited 2021 Jan 24]. Available from: http://www.escholarship.org/uc/item/9qh9h989.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dey PS. Contributions to Stein's method and some limit theorems in probability. [Thesis]. University of California – Berkeley; 2010. Available from: http://www.escholarship.org/uc/item/9qh9h989

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Washington State University

3. [No author]. A Random Matrix Approach to Long Range Acoustic Propagation in the Ocean .

Degree: 2010, Washington State University

URL: http://hdl.handle.net/2376/2795

► Low frequency sound propagates in the ocean within awave guide, formed by the confining effects oftemperature, salinity and pressure on the sound speed.This wave guide… (more)

Subjects/Keywords: internal waves; ocean acoustics; random matrix; statistical model

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APA (6^th Edition):

author], [. (2010). A Random Matrix Approach to Long Range Acoustic Propagation in the Ocean . (Thesis). Washington State University. Retrieved from http://hdl.handle.net/2376/2795

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

author], [No. “A Random Matrix Approach to Long Range Acoustic Propagation in the Ocean .” 2010. Thesis, Washington State University. Accessed January 24, 2021. http://hdl.handle.net/2376/2795.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

author], [No. “A Random Matrix Approach to Long Range Acoustic Propagation in the Ocean .” 2010. Web. 24 Jan 2021.

Vancouver:

author] [. A Random Matrix Approach to Long Range Acoustic Propagation in the Ocean . [Internet] [Thesis]. Washington State University; 2010. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/2376/2795.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. A Random Matrix Approach to Long Range Acoustic Propagation in the Ocean . [Thesis]. Washington State University; 2010. Available from: http://hdl.handle.net/2376/2795

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

4. Zhao, Wei. FROM PINNING TO LOCALIZATION IN GRAPHENE – ON THE STATISTICAL PROPERTIES OF RANDOMLY PINNED FLEXURAL PHONONS.

Degree: PhD, Physics, 2017, Texas A&M University

URL: http://hdl.handle.net/1969.1/173225

► We identify graphene layer on a disordered substrate as a system where Anderson localization of phonons can be observed. Generally, observation of localization for scattering… (more)

Subjects/Keywords: flexrual phonon; Gaussian orthogonal ensemble; nonlinear sigma model; random matrix theory; Graphene; inverse participation ratio; finite difference method; anomalously localized state; weak localization; Anderson localization

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APA (6^th Edition):

Zhao, W. (2017). FROM PINNING TO LOCALIZATION IN GRAPHENE – ON THE STATISTICAL PROPERTIES OF RANDOMLY PINNED FLEXURAL PHONONS. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173225

Chicago Manual of Style (16^th Edition):

Zhao, Wei. “FROM PINNING TO LOCALIZATION IN GRAPHENE – ON THE STATISTICAL PROPERTIES OF RANDOMLY PINNED FLEXURAL PHONONS.” 2017. Doctoral Dissertation, Texas A&M University. Accessed January 24, 2021. http://hdl.handle.net/1969.1/173225.

MLA Handbook (7^th Edition):

Zhao, Wei. “FROM PINNING TO LOCALIZATION IN GRAPHENE – ON THE STATISTICAL PROPERTIES OF RANDOMLY PINNED FLEXURAL PHONONS.” 2017. Web. 24 Jan 2021.

Vancouver:

Zhao W. FROM PINNING TO LOCALIZATION IN GRAPHENE – ON THE STATISTICAL PROPERTIES OF RANDOMLY PINNED FLEXURAL PHONONS. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/1969.1/173225.

Council of Science Editors:

Zhao W. FROM PINNING TO LOCALIZATION IN GRAPHENE – ON THE STATISTICAL PROPERTIES OF RANDOMLY PINNED FLEXURAL PHONONS. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/173225

McMaster University

5. Guo, Wenxing. Advances on Dimension Reduction for Multivariate Linear Regression.

Degree: PhD, 2020, McMaster University

URL: http://hdl.handle.net/11375/25988

► Multivariate linear regression methods are widely used statistical tools in data analysis, and were developed when some response variables are studied simultaneously, in which our… (more)

▼ Multivariate linear regression methods are widely used statistical tools in data analysis, and were developed when some response variables are studied simultaneously, in which our aim is to study the relationship between predictor variables and response variables through the regression coefficient matrix. The rapid improvements of information technology have brought us a large number of large-scale data, but also brought us great challenges in data processing. When dealing with high dimensional data, the classical least squares estimation is not applicable in multivariate linear regression analysis. In recent years, some approaches have been developed to deal with high-dimensional data problems, among which dimension reduction is one of the main approaches. In some literature, random projection methods were used to reduce dimension in large datasets. In Chapter 2, a new random projection method, with low-rank matrix approximation, is proposed to reduce the dimension of the parameter space in high-dimensional multivariate linear regression model. Some statistical properties of the proposed method are studied and explicit expressions are then derived for the accuracy loss of the method with Gaussian random projection and orthogonal random projection. These expressions are precise rather than being bounds up to constants. In multivariate regression analysis, reduced rank regression is also a dimension reduction method, which has become an important tool for achieving dimension reduction goals due to its simplicity, computational efficiency and good predictive performance. In practical situations, however, the performance of the reduced rank estimator is not satisfactory when the predictor variables are highly correlated or the ratio of signal to noise is small. To overcome this problem, in Chapter 3, we incorporate matrix projections into reduced rank regression method, and then develop reduced rank regression estimators based on random projection and orthogonal projection in high-dimensional multivariate linear regression models. We also propose a consistent estimator of the rank of the coefficient matrix and achieve prediction performance bounds for the proposed estimators based on mean squared errors. Envelope technology is also a popular method in recent years to reduce estimative and predictive variations in multivariate regression, including a class of methods to improve the efficiency without changing the traditional objectives. Variable selection is the process of selecting a subset of relevant features variables for use in model construction. The purpose of using this technology is to avoid the curse of dimensionality, simplify models to make them easier to interpret, shorten training time and reduce overfitting. In Chapter 4, we combine envelope models and a group variable selection method to propose an envelope-based sparse reduced rank regression estimator in high-dimensional multivariate linear regression models, and then establish its consistency, asymptotic normality and oracle property. … Advisors/Committee Members: Balakrishnan, Narayanaswamy, Mathematics and Statistics.

Subjects/Keywords: Dimension reduction; Multivariate linear model; Low rank matrix approximation; Random projection; Matrix projection; Reduced rank regression; High-dimensional data; Group LASSO penalty; Variable selection; Sparsity; Envelope model; Multidimensional array; Tensor regression; Sparsity principle.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Guo, W. (2020). Advances on Dimension Reduction for Multivariate Linear Regression. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/25988

Chicago Manual of Style (16^th Edition):

Guo, Wenxing. “Advances on Dimension Reduction for Multivariate Linear Regression.” 2020. Doctoral Dissertation, McMaster University. Accessed January 24, 2021. http://hdl.handle.net/11375/25988.

MLA Handbook (7^th Edition):

Guo, Wenxing. “Advances on Dimension Reduction for Multivariate Linear Regression.” 2020. Web. 24 Jan 2021.

Vancouver:

Guo W. Advances on Dimension Reduction for Multivariate Linear Regression. [Internet] [Doctoral dissertation]. McMaster University; 2020. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/11375/25988.

Council of Science Editors:

Guo W. Advances on Dimension Reduction for Multivariate Linear Regression. [Doctoral Dissertation]. McMaster University; 2020. Available from: http://hdl.handle.net/11375/25988

6. Terreaux, Eugénie. Théorie des Matrices Aléatoires pour l'Imagerie Hyperspectrale : Random Matrix Theory for Hyperspectral Imaging.

Degree: Docteur es, Traitement du signal et des images, 2018, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2018SACLC091

►

La finesse de la résolution spectrale et spatiale des images hyperspectrales en font des données de très grande dimension. C'est également le cas d'autres types… (more)

▼

La finesse de la résolution spectrale et spatiale des images hyperspectrales en font des données de très grande dimension. C'est également le cas d'autres types de données, où leur taille tend à augmenter pour de plus en plus d'applications. La complexité des données provenant de l'hétérogénéité spectrale et spatiale, de la non gaussianité du bruit et des processus physiques sous-jacents, renforcent la richesse des informations présentes sur une image hyperspectrale. Exploiter ces informations demande alors des outils statistiques adaptés aux grandes données mais aussi à leur nature non gaussienne. Des méthodes reposant sur la théorie des matrices aléatoires, théorie adaptée aux données de grande dimension, et reposant sur la robustesse, adaptée aux données non gaussiennes, sont ainsi proposées dans cette thèse, pour des applications à l'imagerie hyperspectrale. Cette thèse propose d'améliorer deux aspects du traitement des images hyperspectrales : l'estimation du nombre d'endmembers ou de l'ordre du modèle et le problème du démélange spectral. En ce qui concerne l'estimation du nombre d'endmembers, trois nouveaux algorithmes adaptés au modèle choisi sont proposés, le dernier présentant de meilleures performances que les deux autres, en raison de sa plus grande robustesse.Une application au domaine de la finance est également proposée. Pour le démélange spectral, trois méthodes sont proposées, qui tiennent comptent des diff érentes particularités possibles des images hyperspectrales. Cette thèse a permis de montrer que la théorie des matrices aléatoires présente un grand intérêt pour le traitement des images hyperspectrales. Les méthodes développées peuvent également s'appliquer à d'autres domaines nécessitant le traitement de données de grandes dimensions.

Hyperspectral imaging generates large data due to the spectral and spatial high resolution, as it is the case for more and more other kinds of applications. For hyperspectral imaging, the data complexity comes from the spectral and spatial heterogeneity, the non-gaussianity of the noise and other physical processes. Nevertheless, this complexity enhances the wealth of collected informations, that need to be processed with adapted methods. Random matrix theory and robust processes are here suggested for hyperspectral imaging application: the random matrix theory is adapted to large data and the robustness enables to better take into account the non-gaussianity of the data. This thesis aims to enhance the model order selection on a hyperspectral image and the unmixing problem. As the model order selection is concerned, three new algorithms are developped, and the last one, more robust, gives better performances. One financial application is also presented. As for the unmixing problem, three methods that take into account the peculierities of hyperspectral imaging are suggested. The random matrix theory is of great interest for hyperspectral image processing, as demonstrated in this thesis. Differents methods developped here can be applied to other field of signal…

Advisors/Committee Members: Ovarlez, Jean-Philippe (thesis director), Pascal, Frédéric (thesis director).

Subjects/Keywords: Théorie des matrices aléatoires; Imagerie hyperspectrale; Modèle linéaire; Bruit CES; Estimation d'ordre de modèle; Démélange spectral; Estimation robuste; Optimisation sous contraintes; Random matrix theory; Hyperspectral imaging; Linear model; CES noise; Model order selection; Unmixing; Robust estimation; Constrained optimization

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APA (6^th Edition):

Terreaux, E. (2018). Théorie des Matrices Aléatoires pour l'Imagerie Hyperspectrale : Random Matrix Theory for Hyperspectral Imaging. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2018SACLC091

Chicago Manual of Style (16^th Edition):

Terreaux, Eugénie. “Théorie des Matrices Aléatoires pour l'Imagerie Hyperspectrale : Random Matrix Theory for Hyperspectral Imaging.” 2018. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed January 24, 2021. http://www.theses.fr/2018SACLC091.

MLA Handbook (7^th Edition):

Terreaux, Eugénie. “Théorie des Matrices Aléatoires pour l'Imagerie Hyperspectrale : Random Matrix Theory for Hyperspectral Imaging.” 2018. Web. 24 Jan 2021.

Vancouver:

Terreaux E. Théorie des Matrices Aléatoires pour l'Imagerie Hyperspectrale : Random Matrix Theory for Hyperspectral Imaging. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2018. [cited 2021 Jan 24]. Available from: http://www.theses.fr/2018SACLC091.

Council of Science Editors:

Terreaux E. Théorie des Matrices Aléatoires pour l'Imagerie Hyperspectrale : Random Matrix Theory for Hyperspectral Imaging. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2018. Available from: http://www.theses.fr/2018SACLC091

University of Technology, Sydney

7. Li, Minqi. Bayesian nonparametric modeling and its applications.

Degree: 2016, University of Technology, Sydney

URL: http://hdl.handle.net/10453/62402

► Bayesian nonparametric methods (or nonparametric Bayesian methods) take the benefit of unlimited parameters and unbounded dimensions to reduce the constraints on the parameter assumption and… (more)

▼ Bayesian nonparametric methods (or nonparametric Bayesian methods) take the benefit of unlimited parameters and unbounded dimensions to reduce the constraints on the parameter assumption and avoid over-fitting implicitly. They have proven to be extremely useful due to their flexibility and applicability to a wide range of problems. In this thesis, we study the Bayesain nonparametric theory with Lévy process and completely random measures (CRM). Several Bayesian nonparametric techniques are presented for computer vision and pattern recognition problems. In particular, our research and contributions focus on the following problems. Firstly, we propose a novel example-based face hallucination method, based on a nonparametric Bayesian model with the assumption that all human faces have similar local pixel structures. We use distance dependent Chinese restaurant process (ddCRP) to cluster the low-resolution (LR) face image patches and give a matrix-normal prior for learning the mapping dictionaries from LR to the corresponding high-resolution (HR) patches. The ddCRP is employed to assist in learning the clusters and mapping dictionaries without setting the number of clusters in advance, such that each dictionary can better reflect the details of the image patches. Experimental results show that our method is efficient and can achieve competitive performance for face hallucination problem. Secondly, we address sparse nonnegative matrix factorization (NMF) problems by using a graph-regularized Beta process (BP) model. BP is a nonparametric method which lets itself naturally model sparse binary matrices with an infinite number of columns. In order to maintain the positivity of the factorized matrices, an exponential prior is proposed. The graph in our model regularizes the similar training samples having similar sparse coefficients. In this way, the structure of the data can be better represented. We demonstrate the effectiveness of our method on different databases. Thirdly, we consider face recognition problem by a nonparametric Bayesian model combined with Sparse Coding Recognition (SCR) framework. In order to get an appropriate dictionary with sparse coefficients, we use a graph regularized Beta process prior for the dictionary learning. The graph in our model regularizes training samples in a same class to have similar sparse coefficients and share similar dictionary atoms. In this way, the proposed method is more robust to noise and occlusion of the testing images. The models in this thesis can also find many other applications like super-resolution, image recognition, text analysis, image compressive sensing and so on.

Subjects/Keywords: Bayesian nonparametric theory.; Lévy process.; Completely random measures (CRM).; Chinese restaurant process (ddCRP).; Low-resolution (LR) face image.; High-resolution (HR) patches.; Nonnegative matrix factorization (NMF).; Beta process (BP) model.; Sparse Coding Recognition (SCR) framework.

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APA (6^th Edition):

Li, M. (2016). Bayesian nonparametric modeling and its applications. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/62402

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Minqi. “Bayesian nonparametric modeling and its applications.” 2016. Thesis, University of Technology, Sydney. Accessed January 24, 2021. http://hdl.handle.net/10453/62402.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Minqi. “Bayesian nonparametric modeling and its applications.” 2016. Web. 24 Jan 2021.

Vancouver:

Li M. Bayesian nonparametric modeling and its applications. [Internet] [Thesis]. University of Technology, Sydney; 2016. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/10453/62402.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li M. Bayesian nonparametric modeling and its applications. [Thesis]. University of Technology, Sydney; 2016. Available from: http://hdl.handle.net/10453/62402

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

8. Katsaounis, Georgios (author). Extended Object Tracking of Pedestrians in Automotive Applications.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749

► Recent advances in sensor technology have lead to increased resolution of novel sensors, while tracking applications where distance between sensors and objects of interest is… (more)

▼ Recent advances in sensor technology have lead to increased resolution of novel sensors, while tracking applications where distance between sensors and objects of interest is very small have gained research interest recently. In these cases, it is possible that multiple sensor detections are generated by each object of interest. Extended Object Tracking (EOT) approaches consist of algorithms which make use of multiple sensor detections per object to jointly estimate their kinematic and shape extent attributes within the Bayesian tracking framework. In the last decade, various EOT algorithms have been proposed for different types of tracking applications. This M.Sc. thesis project addresses the problem of extended tracking of a single pedestrian walking in the area of a stationary vehicle (referred as ego-vehicle in this report) during a real automotive scenario. The objective is to achieve accurate estimation of both the kinematic attributes (2D centroid position/velocity), as well as its shape extent in x-y plane. In more detail, PreScan software is enabled to design a simulation scenario that is very close to a real automotive application, in terms of motion characteristics of objects of interest and sensor data acquisition. In the considered scenario, different sensor modalities are mounted on the ego-vehicle, namely a Lidar sensor and a mono camera sensor. Moreover, OpenPose library is employed to to obtain pose detections of human body parts from obtained camera images. Concerning shape extent representation, the simplest and most popular approach in previous studies, in general and especially for VRUs tracking, is to assume an elliptical shape. In fact, the Random Matrix Model (RMM), proposed originally by Koch, 2008, is a state-of-the-art EOT state modeling approach that allows for joint estimation of centroid kinematics and physical extent for considered elliptical objects of interest. Based on that, a RMM-based filter using Lidar position measurements has been proposed by Feldmann, 2011. In this project, this algorithm is used as a baseline filter for comparison with our proposed algorithm. In addition, an alternative tracking algorithm is proposed in this study, which has the following differences with respect to the baseline filter: State Initialization of the filter: In our proposed version of the tracking algorithm, human pose detections of shoulders and ankles are are associated with obtained Lidar position measurements in order to provide initial values for the kinematic state (2D position/velocity) and shape parameters (ellipse orientation and semi-axes lengths) of the pedestrian.Measurement Update step of the filter: In our proposed version of the tracking algorithm, camera-obtained pose detections of pedestrian shoulders are associated with obtained Lidar position measurements in order to create an extra measurement, for pedestrian heading angle. Subsequently, a nonlinear filtering update step fusing Lidar-obtained point cloud data for pedestrian position and human-pose-obtained… Advisors/Committee Members: Alonso Mora, Javier (mentor), Domhof, Joris (mentor), Tasoglou, Athanasios (mentor), Gavrila, Dariu (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Extended Object Tracking; Vulnurable Road Users; Pedestrians; Environmental Perception; Automotive Applications; Lidar sensor; Mono camera sensor; Sensor Fusion; Random Matrix Model; Elliptical shape; OpenPose library; Human Pose Detections; position measurement; heading angle measurement; Extended Kalman Filter; Kalman Filter

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APA (6^th Edition):

Katsaounis, G. (. (2019). Extended Object Tracking of Pedestrians in Automotive Applications. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749

Chicago Manual of Style (16^th Edition):

Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Masters Thesis, Delft University of Technology. Accessed January 24, 2021. http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749.

MLA Handbook (7^th Edition):

Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Web. 24 Jan 2021.

Vancouver:

Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 24]. Available from: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749.

Council of Science Editors:

Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749

9. Touri, Behrouz. Product of random stochastic matrices and distributed averaging.

Degree: PhD, 0127, 2012, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/29634

► This thesis is mainly concerned with the study of product of random stochastic matrices and random weighted averaging dynamics. It will be shown that a… (more)

▼ This thesis is mainly concerned with the study of product of random stochastic matrices and random weighted averaging dynamics. It will be shown that a generalization of a fundamental result in the theory of ergodic Markov chains not only holds for inhomogeneous chains of stochastic matrices, but also remains true for random stochastic matrices. To do this, the concept of infinite flow property will be introduced for a deterministic chain of stochastic matrices and it will be proven that it is necessary for ergodicity of any stochastic chain. This result will further be extended to ergodic classes, through the development of the concept of the infinite flow graph and ℓ₁-approximation technique. For the converse implications, the product of stochastic matrices will be studied in the more general setting of random adapted stochastic chains. Using a result of A. Kolmogorov, it will be shown that any averaging dynamics admits infinitely many comparison functions including a quadratic one. By identifying the decrease of the quadratic comparison function along the trajectories of the dynamics, it will be proven that under general assumptions on a random chain, the chain is infinite flow stable, i.e. the product of random stochastic matrices is convergent almost surely and, also, the limiting matrices admit certain structures that can be deduced from the infinite flow graph of the chain. It will be shown that a general class of stochastic chains, the balanced chains with feedback property, satisfy the conditions of this result. Some implications of the developed results for products of independent random stochastic matrices will be provided. Furthermore, it will be proven that under general conditions, an independent random chain and its expected chain exhibit the same ergodic behavior. It will be proven that an extension of a well-known result in the theory of homogeneous Markov chains holds for a sequence of inhomogeneous stochastic matrices. Then, link-failure models for averaging dynamics will be introduced and it will be shown that under general conditions, link failure does not affect the limiting behavior of averaging dynamics. Then, the application of the developed methods to the study of Hegselmann-Krause model will be considered. Using the developed results, an upper bound O(m⁴) will be established for the Hegselmann-Krause dynamics, which is an improvement to the previously known bound O(m⁵). As a final application for the developed tools, an alternative proof for the second Borel-Cantelli lemma will be provided. Motivated by the infinite flow property, a stronger one, the absolute infinite flow property will be introduced. It will be shown that this stronger property is in fact necessary for ergodicity of any stochastic chain. Moreover, the equivalency of the absolute infinite flow property with ergodicity of doubly stochastic chains will be proven. These results will be driven by introduction and study of the rotational transformation of a stochastic chain. Finally, motivated by the… Advisors/Committee Members: Nedich, Angelia (advisor), Nedich, Angelia (Committee Chair), Basar, Tamer (committee member), Dullerud, Geir E. (committee member), Kumar, P. R. (committee member), Stipanovi??, Du??an M. (committee member).

Subjects/Keywords: Products of Random Stochastic Matrices; Distributed Averaging; Weighted Averaging; Hegselmann-Krause model; Comparison Functions; Non-negative Matrix Theory

…Rm×m is a random matrix if Wij is a random variable for all i, j ∈ [m]. For a… …k) is a random matrix process, i.e., W (k) is a random matrix for any k ≥ 0… …random stochastic matrices. This mathematical object is one of the main analytical tools that… …row) stochastic matrix for any k ≥ 0 and x(0) ∈ Rm is arbitrary. Some… …studied in [1, 2, 3, 4, 5, 6] both in deterministic time-changing networks and random…

5 more images…

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APA (6^th Edition):

Touri, B. (2012). Product of random stochastic matrices and distributed averaging. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/29634

Chicago Manual of Style (16^th Edition):

Touri, Behrouz. “Product of random stochastic matrices and distributed averaging.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 24, 2021. http://hdl.handle.net/2142/29634.

MLA Handbook (7^th Edition):

Touri, Behrouz. “Product of random stochastic matrices and distributed averaging.” 2012. Web. 24 Jan 2021.

Vancouver:

Touri B. Product of random stochastic matrices and distributed averaging. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/2142/29634.

Council of Science Editors:

Touri B. Product of random stochastic matrices and distributed averaging. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/29634

10. Liu, Yue. Estimation of Two Popular Econometric Models: Random Effects Panel Data Model and Simultaneous Equations Model.

Degree: Applied Statistics, 2013, University of California – Riverside

URL: http://www.escholarship.org/uc/item/3jv029cr

► In the first chapter of this thesis, we propose a penalized splines (P-splines) estimator for random effects panel data model. While being a nonparametric technique,… (more)

Subjects/Keywords: Statistics; Economics; binary dependent variable; corrected asymptotic variance-covariance matrix; P-splines estimator; random effects panel data model; simultaneous equations model; two stage estimation

…Splines Estimator for Random Effects Panel Data Model 4 2.1 Introduction… …18 2.3 Panel Data Model �� ….25 2.4 P-Splines Estimator for Random… …two such advanced econometric models: random effects panel data model and simultaneous… …Estimator for Random Effects Panel Data Model 2.1 Introduction It has long been felt by both… …can be applied to both fixed effects and random effects panel data model. All the…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, Y. (2013). Estimation of Two Popular Econometric Models: Random Effects Panel Data Model and Simultaneous Equations Model. (Thesis). University of California – Riverside. Retrieved from http://www.escholarship.org/uc/item/3jv029cr

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Liu, Yue. “Estimation of Two Popular Econometric Models: Random Effects Panel Data Model and Simultaneous Equations Model.” 2013. Thesis, University of California – Riverside. Accessed January 24, 2021. http://www.escholarship.org/uc/item/3jv029cr.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Liu, Yue. “Estimation of Two Popular Econometric Models: Random Effects Panel Data Model and Simultaneous Equations Model.” 2013. Web. 24 Jan 2021.

Vancouver:

Liu Y. Estimation of Two Popular Econometric Models: Random Effects Panel Data Model and Simultaneous Equations Model. [Internet] [Thesis]. University of California – Riverside; 2013. [cited 2021 Jan 24]. Available from: http://www.escholarship.org/uc/item/3jv029cr.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu Y. Estimation of Two Popular Econometric Models: Random Effects Panel Data Model and Simultaneous Equations Model. [Thesis]. University of California – Riverside; 2013. Available from: http://www.escholarship.org/uc/item/3jv029cr

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

11. Paul, Subhadeep. Consistent community detection in uni-layer and multi-layer networks.

Degree: PhD, Statistics, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/98136

► Over the last two decades, we have witnessed a massive explosion of our data collection abilities and the birth of a "big data" age. This… (more)

▼ Over the last two decades, we have witnessed a massive explosion of our data collection abilities and the birth of a "big data" age. This has led to an enormous interest in statistical inference of a new type of complex data structure, a graph or network. The surge in interdisciplinary interest on statistical analysis of network data has been driven by applications in Neuroscience, Genetics, Social sciences, Computer science, Economics and Marketing. A network consists of a set of nodes or vertices, representing a set of entities, and a set of edges, representing the relations or interactions among the entities. Networks are flexible frameworks that can model many complex systems. In the majority of the network examples dealt with in the literature, the relations between nodes are assumed to be of the same type such as web page linkage, friendship, co-authorship or protein-protein interaction. However, the complex networks in many modern applications are often multi-layered in the sense that they consist of multiple types of edges/relations among a group of entities. Each of those different types of relations can be viewed as creating its own network, called a layer of the multi-layer network. Multi-layer networks are a more accurate representation of many complex systems since many entities in those systems are involved simultaneously in multiple interactions. In this dissertation we view multi-layer networks in the broad sense that includes multiple types of relations as well as multiple information sources on the same set of nodes (e.g., multiple trials or multiple subjects). The problem of detecting communities or clusters of nodes in a network has received considerable attention in literature. As with uni-layer networks, community detection is an important task in multi-layer networks. This dissertation aims to develop new methods and theory for community detection in both uni-layer and multi-layer networks that can be used to answer scientific questions from experimental data. For community detection in uni and multi-layer graphs, we take three approaches - (1) based on statistical random graph models, (2) based on maximizing quality functions, e.g., the modularity score and (3) based on spectral and matrix factorization methods. In Chapter 2 we consider two random graph models for community detection in multi-layer networks, the multi-layer stochastic block model (MLSBM) and a model with a restricted parameter space, the restricted multi-layer stochastic block model (RMLSBM). We derive consistency results for community assignments of the maximum likelihood estimators (MLEs) in both models where MLSBM is assumed to be the true model, and either the number of nodes or the number of types of edges or both grow. We compared MLEs in the two models among themselves and with other baseline approaches both theoretically and through simulations. We also derived minimax error rates and thresholds for achieving consistency of community detection in MLSBM, which were then used to show the advantage of the… Advisors/Committee Members: Chen, Yuguo (advisor), Chen, Yuguo (Committee Chair), Chen, Xiaohui (committee member), Hajek, Bruce (committee member), Portnoy, Stephen (committee member), Simpson, Douglas (committee member).

Subjects/Keywords: Community detection; Consistency; Co-regularization; Invariant subspaces; Minimax rates; Multi-layer networks; Multi-layer null models; Multi-layer modularity; Multi-layer stochastic block model; Network analysis; Non-negative matrix factorization; Neuroimaging; Random effects stochastic block model; Stochastic block model; Spectral clustering; Variational expectation-maximization (EM)

…negative matrix factorization under stochastic block model… …182 Chapter 6 A random effects stochastic block model for joint community detection in… …Stochastic block model DCSBM Degree corrected stochastic block model NMF Non-negative matrix… …Random effects stochastic block model VarEM Variational Expectation-Maximization algorithm… …is a statistical model for random graphs with a natural community structure. It is one of a…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Paul, S. (2017). Consistent community detection in uni-layer and multi-layer networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/98136

Chicago Manual of Style (16^th Edition):

Paul, Subhadeep. “Consistent community detection in uni-layer and multi-layer networks.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 24, 2021. http://hdl.handle.net/2142/98136.

MLA Handbook (7^th Edition):

Paul, Subhadeep. “Consistent community detection in uni-layer and multi-layer networks.” 2017. Web. 24 Jan 2021.

Vancouver:

Paul S. Consistent community detection in uni-layer and multi-layer networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/2142/98136.

Council of Science Editors:

Paul S. Consistent community detection in uni-layer and multi-layer networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/98136

Queensland University of Technology

12. McGarry, Gregory John. Model-based mammographic image analysis.

Degree: 2002, Queensland University of Technology

URL: http://eprints.qut.edu.au/36166/

Subjects/Keywords: Breast Radiography; Radiography, Medical Digital techniques; Image analysis; breast cancer; mammography; radiography; radiology; tissue attenuation; tumour; calcification; parametric model; detection; classification; statistical framework; wavelet transform; multiresolution analysis; generalised Gaussian distribution; method of moments; Wilcoxon signed rank sum test; receiver operating characteristic; clique potential function; Gibbs distribution; co-occurrence matrix; autoregressive; fractal; fractional Brownian motion; maximum likelihood; Markovianity; Gibbs random field; Markov random field; contextual constraints; domain knowledge; maximum a posteriori; segmentation; labelling; iterated conditional modes; maximum pseudo-liklihood; scatter; digitiser; normalisation; least squares; thesis; doctoral

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

McGarry, G. J. (2002). Model-based mammographic image analysis. (Thesis). Queensland University of Technology. Retrieved from http://eprints.qut.edu.au/36166/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

McGarry, Gregory John. “Model-based mammographic image analysis.” 2002. Thesis, Queensland University of Technology. Accessed January 24, 2021. http://eprints.qut.edu.au/36166/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

McGarry, Gregory John. “Model-based mammographic image analysis.” 2002. Web. 24 Jan 2021.

Vancouver:

McGarry GJ. Model-based mammographic image analysis. [Internet] [Thesis]. Queensland University of Technology; 2002. [cited 2021 Jan 24]. Available from: http://eprints.qut.edu.au/36166/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

McGarry GJ. Model-based mammographic image analysis. [Thesis]. Queensland University of Technology; 2002. Available from: http://eprints.qut.edu.au/36166/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations