computational statistics


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computational statistics

[‚käm·pyü′tā·shən·əl stə′tis·tiks]
(statistics)
The conversion of statistical algorithms into computer code that can retrieve useful information from large, complex data sets. Also known as statistical computing.
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While each of these positions has played an important political role in the history of the social sciences, they are each inconsistent with the philosophical foundations of computational statistics.
Trimmed L-Moments: Computational Statistics and Data Analysis, 43(03): 299-314.
Authors Martinez and Martinez present students and instructors with a comprehensive textbook on computational statistics emphasizing the implementation of methods over theoretical concepts.
Research in computational statistics, for example, involves experimental development of visualization and computationally intensive methods for mining large, nonhomo-geneous, multidimensional datasets so as to discover knowledge in the data.
So far, Pidgeon is putting the finishing touches on six WIREs titles with two others in the works:Nanomedicine and Nanobiotechnology, Computational Statistics, Cognitive Science, Systems Biology and Medicine, Computational Molecular Science, and Climate Change.
Robertson is changing his computational statistics PhD framework in real time to take advantage of the breakthrough GIS provides for a nation building by using the ArcGIS bridge with R.
He is a leader in computational statistics and high performance computing, in addition to his extensive expertise in marketing research and in derivative pricing and asset allocation.
provide graduate students in statistics and related fields, statisticians, and quantitative empirical scientists in other fields most of the information they need to develop a broad working knowledge of modern computational statistics.
In addition, techniques related to survey methodology, computational statistics, and operations research are discussed, where applicable.
It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.
It is also a valuable reference for researchers and practitioners in the fields of computational statistics, engineering, and computer science who use statistical modeling techniques.
The strategies discussed are conceptual rather than computational, making the book more accessible; no knowledge of computational statistics is needed.

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