Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family
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, outl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/21/4/348 |
| _version_ | 1828283602012471296 |
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| author | Lei Li Anand N. Vidyashankar Guoqing Diao Ejaz Ahmed |
| author_facet | Lei Li Anand N. Vidyashankar Guoqing Diao Ejaz Ahmed |
| author_sort | Lei Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:47:39Z |
| format | Article |
| id | doaj.art-4849334b25484dd598745cb364d81ac4 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T08:47:39Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-4849334b25484dd598745cb364d81ac42022-12-22T02:53:37ZengMDPI AGEntropy1099-43002019-03-0121434810.3390/e21040348e21040348Robust Inference after Random Projections via Hellinger Distance for Location-Scale FamilyLei Li0Anand N. Vidyashankar1Guoqing Diao2Ejaz Ahmed3Department of Statistics, George Mason University, Fairfax, VA 22030, USADepartment of Statistics, George Mason University, Fairfax, VA 22030, USADepartment of Statistics, George Mason University, Fairfax, VA 22030, USADepartment of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, CanadaBig 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.https://www.mdpi.com/1099-4300/21/4/348compressed dataHellinger distancerepresentation formulaiterated limitsinfluence functionconsistencyasymptotic normalitylocation-scale family |
| spellingShingle | Lei Li Anand N. Vidyashankar Guoqing Diao Ejaz Ahmed Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family Entropy compressed data Hellinger distance representation formula iterated limits influence function consistency asymptotic normality location-scale family |
| title | Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family |
| title_full | Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family |
| title_fullStr | Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family |
| title_full_unstemmed | Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family |
| title_short | Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family |
| title_sort | robust inference after random projections via hellinger distance for location scale family |
| topic | compressed data Hellinger distance representation formula iterated limits influence function consistency asymptotic normality location-scale family |
| url | https://www.mdpi.com/1099-4300/21/4/348 |
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