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 14 of 14 for “"breakdown point"”.
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Real-time implementation of high breakdown point estimators in electric power systems via system decomposition
… decomposition scheme is coupled with a high breakdown point estimator to allow reliable identification of multiple interacting bad data even in cases of conforming errors. The algorithm is inherently resistant to bad measurements in positions of leverage, makes no a priori measurement error …
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Robust, location-free scale estimators for the linear regression and k-sample models
… been quite robust in terms of having a high breakdown point and can achieve a surprisingly high Gaussian efficiency. This idea has also been extended to the simple linear regression model, where typical estimators of the dispersion of the errors depend on an estimator of the regression line. …
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Contributions to Robust Methods: Modified Rank Covariance Matrix and Spatial-EM Algorithm
… of the estimator are studied by the <italic> breakdown point </italic> and <italic> influence function </italic> analysis. More specifically, the finite sample breakdown point is obtained and the upper bound of the finite sample breakdown point can be achieved by a proper choice of univariate …
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Contributions to the theory of statistical breakdown
The theory of statistical breakdown is studied from two different angles. Firstly, the finite sample breakdown points of estimators are found to be inherently related to their tail performances. This connection provides persuasive new evidence for the importance of the breakdown point of estimators …
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Highly Robust and Efficient Estimators of Multivariate Location and Covariance with Applications to Array Processing and Financial Portfolio Optimization
… extends, to the complex-valued domain, the high-breakdown-point class of multivariate estimators called S-estimators. This dissertation defines S-estimators in the complex-valued context, and it defines their properties for complex-valued data. One major shortcoming of the leading …
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Alternativy nejmenších čtverců
… attention to next group of methods whit high breakdown point, which deal with signifi- cance of the individual observations (least weighted squares) and elimination of outlying obser- vations (least trimmed squares). The main purpose of this work is to describe and compare these models, their …
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A Differential Geometry-Based Algorithm for Solving the Minimum Hellinger Distance Estimator
… where the MHDE is shown empirically to have a breakdown point of 50%. A geometric algorithm for solution of the MHDE is developed and implemented. The algorithm utilizes the Riemannian manifold properties of the statistical model to achieve an algorithmic speedup. The MHDE is then applied to an …
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Robust Speech Filter And Voice Encoder Parameter Estimation using the Phase-Phase Correlator
… method outperforms in variance, bias, and breakdown point three other robust approaches based on the arcsin law, the polarity coincidence correlator, and the median- of-ratio estimator without sacrificing the encoder bandwidth efficiency and the compression gain while remaining compatible …
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Robust GM Wiener Filter in the Complex Domain
… array to help remove nonhomogeneous data points from a dataset. Since the early 1970s, STAP has been used in radar systems for their ability to "filter clutter, interference and jamming signals. One major flaw with early STAP radar systems is the reliance on non-robust estimators to …
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Multi-Center Federated Learning to Cluster Clients with non-IID data
… mechanism that can produce a higher breakdown point to tolerate a larger proportion of outliers. All work experiments on three benchmark datasets have demonstrated the effectiveness of the proposed method that outperforms other baseline methods in terms of evaluation criteria.
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Parameter Estimation in Panels of Intercorrelated Time Series
… (2000) to the panel case. We define a panel breakdown point for time series in two ways depending on the type of outliers assumed and compute its value for the panel autocovariance estimator. The estimated autocovariances are then used for the robust parameter estimation. Finally we propose …
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Cluster-Based Bounded Influence Regression
… ordinary least squares estimation while low-breakdown procedures such as M regression and bounded influence regression may be unable to combat a small percentage of outliers. A high-breakdown procedure such as least trimmed squares (LTS) regression can accommodate up to 50% of the data (in …
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Robust techniques and applications in fuzzy clustering
… scheme scales down the FCM memberships of data points based on the distance of the points from the cluster centers. Scaling done on outliers reduces their membership in true clusters. This scheme, known as the Mega-clustering, defines a conceptual mega-cluster which is a collective cluster of …
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Cluster_Based Profile Monitoring in Phase I Analysis
… CBI method provides a robust, efficient and high breakdown regression parameter estimator. The CBI method first represents the data space via a special set of points, referred to as anchor points. Then a collection of single-point-added ordinary least squares regression estimators forms the basis …