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 “"Robust statistics"”.
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Studies in matrix perturbation and robust statistics
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1999.
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Application of robust statistics to asset allocation models
… over such a long period. In this thesis we use robust covariance procedures, such as FAST-MCD, quadrant-correlation-based covariance and 2D-Huber-based covariance, to address the first problem and regularization (Bayesian) methods that fully utilize the market weights of all assets for the …
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Contemporary Approaches to Radar Detection: Robust Statistics, Reinforcement Learning, and Reconfigurable Intelligent Surfaces
… or improve. To this end, we explore the use of robust statistics, reinforcement learning (RL), and reconfigurable intelligent surfaces (RIS) to improve upon existing radar detection techniques. This work focuses only on monostatic co-located MIMO radar and radar networks. First, we look at the …
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Robust state estimation in power systems
The application of robust estimation methods to the power system state estimation problem was investigated. Techniques using both nonlinear and combinatorial optimization were considered, based on the requirements that the method developed should be statistically robust, and fast enough to be used …
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Robust Statistical Radio Interferometric Methods for the Detection of the Epoch of Reionization
… be insurmountable. Automated methods that employ robust statistics will be able to adequately reduce these immense streams of interferometric data to produce uncorrupted estimates, with little to no manual input. This thesis focuses on robust and alternative statistical techniques that better deal …
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Geometric Algorithms for Objects in Motion
… results are presented addressing the issues of robustness, data collection and compression, realistic theoretical analyses of this compression, and data retrieval. Robust statistics is the study of statistical estimators that are robust to data outliers. The combination of robust statistics and …
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Harnessing Sparse and Low-Dimensional Structures for Robust Clustering of Imagery Data
We propose a robust framework for clustering data. In practice, data obtained from real measurement devices can be incomplete, corrupted by gross errors, or not correspond to any assumed model. We show that, by properly harnessing the intrinsic low-dimensional structure of the data, these kinds of …
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Cluster Analysis in High Dimensions: Robustness, Privacy, and Beyond
… focus on the socially motivated constraints of robustness, privacy, and explainability, and how they affect the complexity of these problems. In our quest to understand cluster analysis under such socially motivated constraints, we discover the first black-box transformation from robustness to …
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Measuring heat exchange processes at the air-water interface from thermographic image sequence analysis
… of the noise into account. Methods from robust statistics are employed to correctly solve the estimation problem regardless if the data is corrupted by outliers. The relevance of the developed techniques to other scientific applications is shown. In an accuracy analysis confidence bounds …
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Batch and Online Implicit Weighted Gaussian Processes for Robust Novelty Detection
This dissertation aims mainly at obtaining robust variants of Gaussian processes (GPs) that do not require using non-Gaussian likelihoods to compensate for outliers in the training data. Bayesian kernel methods, and in particular GPs, have been used to solve a variety of machine learning problems, …
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Robust Kalman Filters Using Generalized Maximum Likelihood-Type Estimators
… with non-Gaussian noise exist, a filter that is robust in the presence of outliers and maintains high statistical efficiency is desired. To solve this problem, a new robust Kalman filter framework is proposed that bounds the influence of observation, innovation, and structural outliers in a …
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Performance evaluation and optimization of stochastic systems via importance sampling
… random vectors is obtained by using ideas from robust statistics. All of the above mentioned methods render substantial improvements over standard Monte Carlo simulations when estimating system performance. By incorporating these techniques into both the Robbins-Monro and Kiefer-Wolfowitz …
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Conditioning of FNET Data and Triangulation of Generator Trips in the Eastern Interconnected System
… by the FDRs must first be conditioned in a robust manner. The current method that uses the moving mean of raw FDR data is analyzed and two computationally efficient robust methods are suggested in this report. These new methods that rely on robust statistics are more resistant to the effect …
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Robust Adaptive Signal Processors
… the N-dimensional input interference and noise statistics. Often, estimated statistics are biased by contaminant data (such as outliers and non-stationary data) that do not fit the dominant distribution, which is often modeled as Gaussian. In particular, convergence of sample covariance matrices …
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Numerical Contributions to the Asymptotic Theory of Robustness
… package – the R bundle RobASt – by means of the statistics software R has been developed. It includes all robust procedures introduced throughout the thesis. The dissertation itself consists of five parts and starts with a brief motivation, which makes precise why robust statistics is necessary. …
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Decision-making under statistical uncertainty
… on the asymptotic behavior of the two test statistics to explain the advantage of the GLRT. We then study robust procedures for universal hypothesis testing when there is uncertainty about the null hypothesis. We present new results on the asymptotic behavior of the proposed test statistic …
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Robust speech filtering in impulsive noise environments
This thesis presents a new robust filtering technique that suppresses impulsive noise in speech signals. The method makes use of Projection Statistics based on medians to detect segments of speech with impulses. The autoregressive model employed to smooth out the speech signal is identified by …
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Extending linear grouping analysis and robust estimators for very large data sets
… optimization problems in the field of robust statistics, and demonstrate, via simulation study as well as application on actual data sets, that the BIRCH solution compares favourably to the existing state-of-the-art alternatives, and in many cases finds a more optimal solution.
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Improved Guarantees for Learning GMMs
… in a wide variety of fields including statistics, biology, physics and computer science. A fundamental task at the core of many of these applications is to learn the parameters of a mixture of Gaussians from samples. Starting with the seminal work of Karl Pearson in 1894 [81], there has …
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Estimation-theoretic framework for robust and energy-efficient system design
… SoCs, we have also identified an important robust estimation problem that has remained largely unaddressed within the robust statistics community. To address this need, new methods for robust estimation with correlated observations were developed that could be applicable to more general …
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