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University of Michigan

1. McGaffin, Madison G. X-ray CT Image Reconstruction on Highly-Parallel Architectures.

Degree: PhD, Electrical Engineering: Systems, 2015, University of Michigan

URL: http://hdl.handle.net/2027.42/113551

► Model-based image reconstruction (MBIR) methods for X-ray CT use accurate models of the CT acquisition process, the statistics of the noisy measurements, and noise-reducing regularization…
(more)

Subjects/Keywords: Model-based image reconstruction; X-ray CT; Parallel computing; Electrical Engineering; Engineering

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APA (6^{th} Edition):

McGaffin, M. G. (2015). X-ray CT Image Reconstruction on Highly-Parallel Architectures. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113551

Chicago Manual of Style (16^{th} Edition):

McGaffin, Madison G. “X-ray CT Image Reconstruction on Highly-Parallel Architectures.” 2015. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/113551.

MLA Handbook (7^{th} Edition):

McGaffin, Madison G. “X-ray CT Image Reconstruction on Highly-Parallel Architectures.” 2015. Web. 22 Aug 2019.

Vancouver:

McGaffin MG. X-ray CT Image Reconstruction on Highly-Parallel Architectures. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/113551.

Council of Science Editors:

McGaffin MG. X-ray CT Image Reconstruction on Highly-Parallel Architectures. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113551

University of Michigan

2. Roy, Sandipan. Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure.

Degree: PhD, Statistics, 2015, University of Michigan

URL: http://hdl.handle.net/2027.42/113602

► New technological advancements have allowed collection of datasets of large volume and different levels of complexity. Many of these datasets have an underlying network structure.…
(more)

Subjects/Keywords: Network; Heterogeneous; High-dimernsional; Subsampling; Statistics and Numeric Data; Science

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APA (6^{th} Edition):

Roy, S. (2015). Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113602

Chicago Manual of Style (16^{th} Edition):

Roy, Sandipan. “Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure.” 2015. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/113602.

MLA Handbook (7^{th} Edition):

Roy, Sandipan. “Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure.” 2015. Web. 22 Aug 2019.

Vancouver:

Roy S. Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/113602.

Council of Science Editors:

Roy S. Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113602

University of Michigan

3. Vandermeulen, Robert A. Functional Analytic Perspectives on Nonparametric Density Estimation.

Degree: PhD, Electrical Engineering: Systems, 2016, University of Michigan

URL: http://hdl.handle.net/2027.42/133205

► Nonparametric density estimation is a classic problem in statistics. In the standard estimation setting, when one has access to iid samples from an unknown distribution,…
(more)

Subjects/Keywords: Nonparametric Statistics; Machine Learning; Electrical Engineering; Engineering

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APA (6^{th} Edition):

Vandermeulen, R. A. (2016). Functional Analytic Perspectives on Nonparametric Density Estimation. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133205

Chicago Manual of Style (16^{th} Edition):

Vandermeulen, Robert A. “Functional Analytic Perspectives on Nonparametric Density Estimation.” 2016. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/133205.

MLA Handbook (7^{th} Edition):

Vandermeulen, Robert A. “Functional Analytic Perspectives on Nonparametric Density Estimation.” 2016. Web. 22 Aug 2019.

Vancouver:

Vandermeulen RA. Functional Analytic Perspectives on Nonparametric Density Estimation. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/133205.

Council of Science Editors:

Vandermeulen RA. Functional Analytic Perspectives on Nonparametric Density Estimation. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133205

University of Michigan

4. Jin, Curtis. New Methods and Theory for Increasing Transmission of Light through Highly-Scattering Random Media.

Degree: PhD, Electrical Engineering: Systems, 2014, University of Michigan

URL: http://hdl.handle.net/2027.42/107051

► Scattering hinders the passage of light through random media and consequently limits the usefulness of optical techniques for sensing and imaging. Thus, methods for increasing…
(more)

Subjects/Keywords: Wave Propagation; Scattering; Random Matrix Theory; Numerical Methods; Iterative Methods; Statistical Signal Processing; Electrical Engineering; Engineering; Science

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APA (6^{th} Edition):

Jin, C. (2014). New Methods and Theory for Increasing Transmission of Light through Highly-Scattering Random Media. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/107051

Chicago Manual of Style (16^{th} Edition):

Jin, Curtis. “New Methods and Theory for Increasing Transmission of Light through Highly-Scattering Random Media.” 2014. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/107051.

MLA Handbook (7^{th} Edition):

Jin, Curtis. “New Methods and Theory for Increasing Transmission of Light through Highly-Scattering Random Media.” 2014. Web. 22 Aug 2019.

Vancouver:

Jin C. New Methods and Theory for Increasing Transmission of Light through Highly-Scattering Random Media. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/107051.

Council of Science Editors:

Jin C. New Methods and Theory for Increasing Transmission of Light through Highly-Scattering Random Media. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/107051

5. Le, Can M. Estimating Community Structure in Networks by Spectral Methods.

Degree: PhD, Statistics, 2016, University of Michigan

URL: http://hdl.handle.net/2027.42/133258

► Networks are studied in a wide range of fields, including social psychology, sociology, physics, computer science, probability, and statistics. One of the fundamental problems in…
(more)

Subjects/Keywords: Network analysis; Community detection; Concentration of sparse random graphs; Computer Science; Mathematics; Science (General); Statistics and Numeric Data; Engineering; Science

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APA (6^{th} Edition):

Le, C. M. (2016). Estimating Community Structure in Networks by Spectral Methods. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133258

Chicago Manual of Style (16^{th} Edition):

Le, Can M. “Estimating Community Structure in Networks by Spectral Methods.” 2016. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/133258.

MLA Handbook (7^{th} Edition):

Le, Can M. “Estimating Community Structure in Networks by Spectral Methods.” 2016. Web. 22 Aug 2019.

Vancouver:

Le CM. Estimating Community Structure in Networks by Spectral Methods. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/133258.

Council of Science Editors:

Le CM. Estimating Community Structure in Networks by Spectral Methods. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133258

6. Moon, Kevin R. Nonparametric Estimation of Distributional Functionals and Applications.

Degree: PhD, Electrical Engineering: Systems, 2016, University of Michigan

URL: http://hdl.handle.net/2027.42/133394

► Distributional functionals are integrals of functionals of probability densities and include functionals such as information divergence, mutual information, and entropy. Distributional functionals have many applications…
(more)

Subjects/Keywords: divergence estimation; mutual information estimation; entropy estimation; ensemble estimation; active region; high frequency oscillation; Electrical Engineering; Engineering

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APA (6^{th} Edition):

Moon, K. R. (2016). Nonparametric Estimation of Distributional Functionals and Applications. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133394

Chicago Manual of Style (16^{th} Edition):

Moon, Kevin R. “Nonparametric Estimation of Distributional Functionals and Applications.” 2016. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/133394.

MLA Handbook (7^{th} Edition):

Moon, Kevin R. “Nonparametric Estimation of Distributional Functionals and Applications.” 2016. Web. 22 Aug 2019.

Vancouver:

Moon KR. Nonparametric Estimation of Distributional Functionals and Applications. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/133394.

Council of Science Editors:

Moon KR. Nonparametric Estimation of Distributional Functionals and Applications. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133394

7. Asendorf, Nicholas A. Informative Data Fusion: Beyond Canonical Correlation Analysis.

Degree: PhD, Electrical Engineering: Systems, 2015, University of Michigan

URL: http://hdl.handle.net/2027.42/113419

► Multi-modal data fusion is a challenging but common problem arising in fields such as economics, statistical signal processing, medical imaging, and machine learning. In such…
(more)

Subjects/Keywords: Correlation analysis; Random matrix theory; Electrical Engineering; Engineering

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APA (6^{th} Edition):

Asendorf, N. A. (2015). Informative Data Fusion: Beyond Canonical Correlation Analysis. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113419

Chicago Manual of Style (16^{th} Edition):

Asendorf, Nicholas A. “Informative Data Fusion: Beyond Canonical Correlation Analysis.” 2015. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/113419.

MLA Handbook (7^{th} Edition):

Asendorf, Nicholas A. “Informative Data Fusion: Beyond Canonical Correlation Analysis.” 2015. Web. 22 Aug 2019.

Vancouver:

Asendorf NA. Informative Data Fusion: Beyond Canonical Correlation Analysis. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/113419.

Council of Science Editors:

Asendorf NA. Informative Data Fusion: Beyond Canonical Correlation Analysis. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113419

8. Matakos, Antonios. Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories.

Degree: PhD, Electrical Engineering-Systems, 2013, University of Michigan

URL: http://hdl.handle.net/2027.42/102449

► In susceptibility-weighted MRI, ignoring the magnetic field inhomogeneity can lead to severe reconstruction artifacts. Correcting for the effects of magnetic field inhomogeneity requires accurate fieldmaps.…
(more)

Subjects/Keywords: Magnetic Resonance Imaging (MRI); Echo-Planar Imaging (EPI); EPI Ghost Correction; Joint Estimation; Through-plane Fieldmap Gradients; Augmented Lagrangian (AL); Electrical Engineering; Engineering

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APA (6^{th} Edition):

Matakos, A. (2013). Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/102449

Chicago Manual of Style (16^{th} Edition):

Matakos, Antonios. “Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories.” 2013. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/102449.

MLA Handbook (7^{th} Edition):

Matakos, Antonios. “Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories.” 2013. Web. 22 Aug 2019.

Vancouver:

Matakos A. Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories. [Internet] [Doctoral dissertation]. University of Michigan; 2013. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/102449.

Council of Science Editors:

Matakos A. Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories. [Doctoral Dissertation]. University of Michigan; 2013. Available from: http://hdl.handle.net/2027.42/102449

9. Xu, Kevin S. Computational Methods for Learning and Inference on Dynamic Networks.

Degree: PhD, Electrical Engineering-Systems, 2012, University of Michigan

URL: http://hdl.handle.net/2027.42/94022

► Networks are ubiquitous in science, serving as a natural representation for many complex physical, biological, and social phenomena. Significant efforts have been dedicated to analyzing…
(more)

Subjects/Keywords: Dynamic Networks; Network Models; Community Detection; Graph Layout; Machine Learning; Social and Information Networks; Computer Science; Electrical Engineering; Engineering

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APA (6^{th} Edition):

Xu, K. S. (2012). Computational Methods for Learning and Inference on Dynamic Networks. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/94022

Chicago Manual of Style (16^{th} Edition):

Xu, Kevin S. “Computational Methods for Learning and Inference on Dynamic Networks.” 2012. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/94022.

MLA Handbook (7^{th} Edition):

Xu, Kevin S. “Computational Methods for Learning and Inference on Dynamic Networks.” 2012. Web. 22 Aug 2019.

Vancouver:

Xu KS. Computational Methods for Learning and Inference on Dynamic Networks. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/94022.

Council of Science Editors:

Xu KS. Computational Methods for Learning and Inference on Dynamic Networks. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/94022

10. Hwang, Sung Jin. Geometric Representations of High Dimensional Random Data.

Degree: PhD, Electrical Engineering-Systems, 2012, University of Michigan

URL: http://hdl.handle.net/2027.42/96097

► This thesis introduces geometric representations relevant to the analysis of datasets of random vectors in high dimension. These representations are used to study the behavior…
(more)

Subjects/Keywords: High Dimensional Data; Engineering

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APA (6^{th} Edition):

Hwang, S. J. (2012). Geometric Representations of High Dimensional Random Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/96097

Chicago Manual of Style (16^{th} Edition):

Hwang, Sung Jin. “Geometric Representations of High Dimensional Random Data.” 2012. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/96097.

MLA Handbook (7^{th} Edition):

Hwang, Sung Jin. “Geometric Representations of High Dimensional Random Data.” 2012. Web. 22 Aug 2019.

Vancouver:

Hwang SJ. Geometric Representations of High Dimensional Random Data. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/96097.

Council of Science Editors:

Hwang SJ. Geometric Representations of High Dimensional Random Data. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/96097

11. Tsiligkaridis, Theodoros. High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing.

Degree: PhD, Electrical Engineering: Systems, 2014, University of Michigan

URL: http://hdl.handle.net/2027.42/107110

► This thesis makes contributions to a fundamental set of high dimensional problems in the following areas: (1) performance bounds for high dimensional estimation of structured…
(more)

Subjects/Keywords: Separable Models for Covariance Estimation and Controlled Sensing; Convergence Theory for Decentralized Controlled Sensing; Collaborative Signal Processing; Electrical Engineering; Engineering

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APA (6^{th} Edition):

Tsiligkaridis, T. (2014). High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/107110

Chicago Manual of Style (16^{th} Edition):

Tsiligkaridis, Theodoros. “High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing.” 2014. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/107110.

MLA Handbook (7^{th} Edition):

Tsiligkaridis, Theodoros. “High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing.” 2014. Web. 22 Aug 2019.

Vancouver:

Tsiligkaridis T. High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/107110.

Council of Science Editors:

Tsiligkaridis T. High Dimensional Separable Representations for Statistical Estimation and Controlled Sensing. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/107110

12. Newstadt, Gregory Evan. Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars.

Degree: PhD, Electrical Engineering-Systems, 2013, University of Michigan

URL: http://hdl.handle.net/2027.42/97931

► This thesis studies adaptive allocation of a limited set of sensing or computational resources in order to maximize some criteria, such as detection probability, estimation…
(more)

Subjects/Keywords: Adaptive Sensing; Sensor Managment; Synthetic Aperture Radar; Hierarchical Bayesian Models; Electrical Engineering; Engineering

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APA (6^{th} Edition):

Newstadt, G. E. (2013). Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/97931

Chicago Manual of Style (16^{th} Edition):

Newstadt, Gregory Evan. “Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars.” 2013. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/97931.

MLA Handbook (7^{th} Edition):

Newstadt, Gregory Evan. “Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars.” 2013. Web. 22 Aug 2019.

Vancouver:

Newstadt GE. Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars. [Internet] [Doctoral dissertation]. University of Michigan; 2013. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/97931.

Council of Science Editors:

Newstadt GE. Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars. [Doctoral Dissertation]. University of Michigan; 2013. Available from: http://hdl.handle.net/2027.42/97931

13. Park, Se Un. Reconstruction, Classification, and Segmentation for Computational Microscopy.

Degree: PhD, Electrical Engineering: Systems, 2013, University of Michigan

URL: http://hdl.handle.net/2027.42/102296

► This thesis treats two fundamental problems in computational microscopy: image reconstruction for magnetic resonance force microscopy (MRFM) and image classification for electron backscatter diffraction (EBSD).…
(more)

Subjects/Keywords: Bayesian Image Reconstruction; Blind Deconvolution; Computational Microscopy: Magnetic Resonance Force Microscopy (MRFM) and Electron Backscatter Diffraction (EBSD); Dictionary-based Classification, Segmentation, and Anomaly Detection; Markov Chain Monte Carlo and Variational Bayes Methods; Uncertainty Quantification and Super-resolution; Electrical Engineering; Engineering

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APA (6^{th} Edition):

Park, S. U. (2013). Reconstruction, Classification, and Segmentation for Computational Microscopy. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/102296

Chicago Manual of Style (16^{th} Edition):

Park, Se Un. “Reconstruction, Classification, and Segmentation for Computational Microscopy.” 2013. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/102296.

MLA Handbook (7^{th} Edition):

Park, Se Un. “Reconstruction, Classification, and Segmentation for Computational Microscopy.” 2013. Web. 22 Aug 2019.

Vancouver:

Park SU. Reconstruction, Classification, and Segmentation for Computational Microscopy. [Internet] [Doctoral dissertation]. University of Michigan; 2013. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/102296.

Council of Science Editors:

Park SU. Reconstruction, Classification, and Segmentation for Computational Microscopy. [Doctoral Dissertation]. University of Michigan; 2013. Available from: http://hdl.handle.net/2027.42/102296

14. Liu, Zhipeng. Discrete Toeplitz Determinants and their Applications.

Degree: PhD, Mathematics, 2014, University of Michigan

URL: http://hdl.handle.net/2027.42/108904

► In this dissertation, we consider the asymptotics of discrete Toeplitz determinants. We find a simple identity which express a discrete Toeplitz determinant as the product…
(more)

Subjects/Keywords: Discrete Toeplitz Determinant; Nonintersecting Process; Airy Process; Mathematics; Science

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APA (6^{th} Edition):

Liu, Z. (2014). Discrete Toeplitz Determinants and their Applications. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/108904

Chicago Manual of Style (16^{th} Edition):

Liu, Zhipeng. “Discrete Toeplitz Determinants and their Applications.” 2014. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/108904.

MLA Handbook (7^{th} Edition):

Liu, Zhipeng. “Discrete Toeplitz Determinants and their Applications.” 2014. Web. 22 Aug 2019.

Vancouver:

Liu Z. Discrete Toeplitz Determinants and their Applications. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/108904.

Council of Science Editors:

Liu Z. Discrete Toeplitz Determinants and their Applications. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/108904

15. Chen, Yilun. Regularized Estimation of High-dimensional Covariance Matrices.

Degree: PhD, Electrical Engineering: Systems, 2011, University of Michigan

URL: http://hdl.handle.net/2027.42/86396

► Many signal processing methods are fundamentally related to the estimation of covariance matrices. In cases where there are a large number of covariates the dimension…
(more)

Subjects/Keywords: High-dimensional; Covariance Matrix Estimation; Compressive Sensing; Recursive Group Lasso; Sparse Least-Mean-Square; Analog-to-Digital Converter; Electrical Engineering; Engineering

…between *University* *of* *Michigan* (Yilun Chen, Prof. Alfred Hero) and Technion
– Israel…

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APA (6^{th} Edition):

Chen, Y. (2011). Regularized Estimation of High-dimensional Covariance Matrices. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/86396

Chicago Manual of Style (16^{th} Edition):

Chen, Yilun. “Regularized Estimation of High-dimensional Covariance Matrices.” 2011. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/86396.

MLA Handbook (7^{th} Edition):

Chen, Yilun. “Regularized Estimation of High-dimensional Covariance Matrices.” 2011. Web. 22 Aug 2019.

Vancouver:

Chen Y. Regularized Estimation of High-dimensional Covariance Matrices. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/86396.

Council of Science Editors:

Chen Y. Regularized Estimation of High-dimensional Covariance Matrices. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/86396

16. Tibau Puig, Arnau. Learning from High-Dimensional Multivariate Signals.

Degree: PhD, Electrical Engineering: Systems, 2012, University of Michigan

URL: http://hdl.handle.net/2027.42/91544

► Modern measurement systems monitor a growing number of variables at low cost. In the problem of characterizing the observed measurements, budget limitations usually constrain the…
(more)

Subjects/Keywords: Statistical Signal Processing; Electrical Engineering; Engineering

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APA (6^{th} Edition):

Tibau Puig, A. (2012). Learning from High-Dimensional Multivariate Signals. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/91544

Chicago Manual of Style (16^{th} Edition):

Tibau Puig, Arnau. “Learning from High-Dimensional Multivariate Signals.” 2012. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/91544.

MLA Handbook (7^{th} Edition):

Tibau Puig, Arnau. “Learning from High-Dimensional Multivariate Signals.” 2012. Web. 22 Aug 2019.

Vancouver:

Tibau Puig A. Learning from High-Dimensional Multivariate Signals. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/91544.

Council of Science Editors:

Tibau Puig A. Learning from High-Dimensional Multivariate Signals. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/91544

17. Martin, Travis Bennett. Theoretical Tools for Network Analysis: Game Theory, Graph Centrality, and Statistical Inference.

Degree: PhD, Computer Science and Engineering, 2016, University of Michigan

URL: http://hdl.handle.net/2027.42/133463

► A computer-driven data explosion has made the difficulty of interpreting large data sets of interconnected entities ever more salient. My work focuses on theoretical tools…
(more)

Subjects/Keywords: Network science; Computer Science; Engineering

…network of all college students, students at
*University* *of* *Michigan* (UM) display…

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APA (6^{th} Edition):

Martin, T. B. (2016). Theoretical Tools for Network Analysis: Game Theory, Graph Centrality, and Statistical Inference. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133463

Chicago Manual of Style (16^{th} Edition):

Martin, Travis Bennett. “Theoretical Tools for Network Analysis: Game Theory, Graph Centrality, and Statistical Inference.” 2016. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/133463.

MLA Handbook (7^{th} Edition):

Martin, Travis Bennett. “Theoretical Tools for Network Analysis: Game Theory, Graph Centrality, and Statistical Inference.” 2016. Web. 22 Aug 2019.

Vancouver:

Martin TB. Theoretical Tools for Network Analysis: Game Theory, Graph Centrality, and Statistical Inference. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/133463.

Council of Science Editors:

Martin TB. Theoretical Tools for Network Analysis: Game Theory, Graph Centrality, and Statistical Inference. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133463

18. Kallur Palli Kumar, Sricharan. Neighborhood Graphs for Estimation of Density Functionals.

Degree: PhD, Electrical Engineering-Systems, 2012, University of Michigan

URL: http://hdl.handle.net/2027.42/93940

► Functionals of densities play a fundamental role in statistics, signal processing, machine learning, information theory and related fields. This class of functionals includes divergence measures…
(more)

Subjects/Keywords: K Nearest Neighbor; K-NN Graphs; K-NN Estimators; Ensemble Estimators; Entropy Estimation; Dimension Estimation; Electrical Engineering; Engineering

…Report, Communications and Signal
Processing Laboratory (CSPL), The *University* *of*… …*Michigan*, December 2010.
9. K. Sricharan, R. Raich and A. O. Hero, ”Boundary compensated k-NN…

Record Details Similar Records

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

APA (6^{th} Edition):

Kallur Palli Kumar, S. (2012). Neighborhood Graphs for Estimation of Density Functionals. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/93940

Chicago Manual of Style (16^{th} Edition):

Kallur Palli Kumar, Sricharan. “Neighborhood Graphs for Estimation of Density Functionals.” 2012. Doctoral Dissertation, University of Michigan. Accessed August 22, 2019. http://hdl.handle.net/2027.42/93940.

MLA Handbook (7^{th} Edition):

Kallur Palli Kumar, Sricharan. “Neighborhood Graphs for Estimation of Density Functionals.” 2012. Web. 22 Aug 2019.

Vancouver:

Kallur Palli Kumar S. Neighborhood Graphs for Estimation of Density Functionals. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2019 Aug 22]. Available from: http://hdl.handle.net/2027.42/93940.

Council of Science Editors:

Kallur Palli Kumar S. Neighborhood Graphs for Estimation of Density Functionals. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/93940