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Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family

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dc.contributor.author Li, Lei
dc.contributor.author Vidyashankar, Anand N.
dc.contributor.author Diao, Guoqing
dc.contributor.author Ahmed, Ejaz
dc.date.accessioned 2019-07-02T13:15:39Z
dc.date.available 2019-07-02T13:15:39Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/1920/11507
dc.description.abstract Big data and streaming data are encountered in a variety of contemporary applications in business and industry. In such cases, it is common to use random projections to reduce the dimension of the data yielding compressed data. These data however possess various anomalies such as heterogeneity, outliers, and round-off errors which are hard to detect due to volume and processing challenges. This paper describes a new robust and efficient methodology, using Hellinger distance, to analyze the compressed data. Using large sample methods and numerical experiments, it is demonstrated that a routine use of robust estimation procedure is feasible. The role of double limits in understanding the efficiency and robustness is brought out, which is of independent interest
dc.language.iso en_US en_US
dc.publisher Entropy en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/us/ *
dc.title Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family en_US
dc.type Article en_US
dc.identifier.doi 10.3390/e21040348


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