
High dimensional statistical inference: theoretical development to data analytics
This article is due to appear in the Handbook of Statistics, Vol. 43, El...
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A New Approach of Exploiting SelfAdjoint Matrix Polynomials of Large Random Matrices for Anomaly Detection and Fault Location
Synchronized measurements of a large power grid enable an unprecedented ...
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A Datadriven Approach to Multievent Analytics in Largescale Power Systems Using Factor Model
Multievent detection and recognition in real time is of challenge for a...
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Big data analytics architecture design
Objective. We propose an approach to reason about goals, obstacles, and ...
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Learningbased Automatic Parameter Tuning for Big Data Analytics Frameworks
Big data analytics frameworks (BDAFs) have been widely used for data pro...
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Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications
Statistical analysis (SA) is a complex process to deduce population prop...
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Two Dimensional Stochastic Configuration Networks for Image Data Analytics
Stochastic configuration networks (SCNs) as a class of randomized learne...
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A Hybrid Framework for Topology Identification of Distribution Grid with Renewables Integration
Topology identification (TI) is a key task for state estimation (SE) in distribution grids, especially the one with highpenetration renewables. The uncertainties, initiated by the timeseries behavior of renewables, will almost certainly lead to bad TI results without a proper treatment. These uncertainties are analytically intractable under conventional framework–they are usually jointly spatialtemporal dependent, and hence cannot be simply treated as white noise. For this purpose, a hybrid framework is suggested in this paper to handle these uncertainties in a systematic and theoretical way; in particular, big data analytics are studied to harness the jointly spatialtemporal statistical properties of those uncertainties. With some prior knowledge, a model bank is built first to store the countable typical models of network configurations; therefore, the difference between the SE outputs of each bank model and our observation is capable of being defined as a matrix variate–the socalled random matrix. In order to gain insight into the random matrix, a welldesigned metric space is needed. Autoregression (AR) model, factor analysis (FA), and random matrix theory (RMT) are tied together for the metric space design, followed by jointly temporalspatial analysis of those matrices which is conducted in a highdimensional (vector) space. Under the proposed framework, some big data analytics and theoretical results are obtained to improve the TI performance. Our framework is validated using IEEE standard distribution network with some field data in practice.
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