Radhakrishna Rao, C. (2001) Reflections on the past and visions for the future Communications in Statistics - Theory and Methods, 30 (11). pp. 2235-2257. ISSN 0361-0926
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Official URL: http://www.tandfonline.com/doi/abs/10.1081/STA-100...
Related URL: http://dx.doi.org/10.1081/STA-100107683
Abstract
Statistics as data is ancient, but as a discipline of study and research it has a short history. Courses leading to degrees in statistics have been introduced in universities some sixty to seventy years ago. They were not considered to constitute a basic discipline with a subject matter of its own. However, during the last seventy five years, it has developed as a powerful blend of science, technology and art for solving problems in all areas of human endeavor. Now-a-days statistics is used in scientific research, economic development through optimum use of resources, increasing industrial productivity, medical diagnosis, legal practice, disputed authorship, and optimum decision making at individual and institutional levels. What is the future of statistics in the coming millennium dominated by information technology encompassing the whole of communications, interaction with intelligent systems, massive data bases, and complex information processing networks? The current statistical methodology based on probabilistic models applied on small data sets appears to be inadequate to meet the needs of the society in terms of quick processing of data and making the information available for practical purposes. Adhoc methods are being put forward under the title Data Mining by computer scientists and engineers to meet the needs of customers. The paper reviews the current state of the art in statistics and discusses possible future developments considering the availability of large data sets, enormous computing power and efficient optimization techniques using genetic algorithms and neural networks.
Item Type: | Article |
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Source: | Copyright of this article belongs to Taylor and Francis Group. |
Keywords: | Bayesian Methods; Data Mining; Decision Theory; Hypothesis Testing; Large Data Sets; Machine Learning; Neural Networks |
ID Code: | 71904 |
Deposited On: | 28 Nov 2011 04:22 |
Last Modified: | 28 Nov 2011 04:22 |
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