Outlier Identification and Large-Scale Inference
Date Issued
2015
Date
2015
Author(s)
Wu, Ming-Chun
Abstract
Outlier detection is a frequently encountered technology challenge for many diverse applications, and remains an open problem in general. There are two major difficulties of developing outlier detectors with collected data. One is the inevitable data reduction, the other is the effective inference when discovering information from unknown structured large-scale data. It is even more interesting and challenging with limited observation, since conventional data analysis requires many samples to achieve a satisfactory performance. In this thesis, a sensor network example is used to illustrate a general inference procedure for outlier identification. A systematic framework is proposed to develop effective and efficient outlier identifiers using shrinkage methodology, like James-Stein estimator, as the post-processor. This thesis show the superiority of our approach, particularly for the large-scale situations. This thesis further supply a water-filling type algorithm to obtain the asymptotic optimal method for a general class of shrinkage estimators, for wide applications of data analysis.
Subjects
Outlier detection
James-Stein estimator
empirical Bayesian
shrinkage estimator
large-scale inference
Type
thesis
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