1 edition of Antisampling for estimation found in the catalog.
Antisampling for estimation
Neil C. Rowe
We survey a new way to get quick estimates of the values of simple statistics (like count, mean, standard deviation, maximum, median, and mode frequency) on a large data set. This approach is a comprehensive attempt (apparently the first) to estimate statistics without any sampling, by reasoning about various sets containing a population interest. Our antisampling techniques have connections to those of sampling (and have duals in many cases), but they have different advantages and disadvantages, making antisampling sometimes preferable to sampling, sometimes not. In particular, they can only be efficient when data is in a computer, and they exploit computer science ideas such as production systems and database theory. Antisampling also requires the overhead of construction of an auxiliary structure, a database abstract . Tests on sample data show similar or better performance than simple random sampling. We also discuss more complex methods of sampling and their disadvantages.
|Statement||[by] Neil C. Rowe|
|Contributions||Naval Postgraduate School (U.S.)|
|The Physical Object|
|Pagination||24 p. :|
|Number of Pages||24|
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Absolute bounds (or inequalities) on statistical quantities are often a desirable feature of statistical packages since, as contrasted with estimates of Antisampling for estimation book same quantities, they can avoid distributional assumptions and can often be calculated very fast.
We investigate bounds on the mean and standard deviation of transformed data values, given only a few statistics Cited by: Our "antisampling" techniques have analogies to those of sampling, and exhibit similar estimation accuracy, but can be done much faster than sampling with large computer databases.
A fourth application is to 'antisampling' techniques for estimating values of statistics on a database . Antisampling is like an opposite of sampling, obtaining its estimates from reasoning about previously computed statistics rather than samples of the data; it can be much faster than sampling when data is kept in secondary by: ELSEVIER Decision Support Systems 15 () sun g Data requirements in statistical decision support systems: Formulation and some results in choosing summaries Terry Barron a,*, A.N.
Saharia b a Department of Information Systems and Operations Management, Stranahan HallCollege of Business Administration, Uniuersity of Toledo, Cited by: 7.
N. Rowe, “Antisampling for Estimation: An Over-view”, IEEE transactions on Software Engineering, pp. –, October Google Scholar. Bridgeport Music, Inc. Dimension Films, F.3dn (6th Cir. ); see also Hannibal Travis, Google Book Search and Fair Use: iTunes for Authors, or Napster for Books?, 61 U.
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In Computer science and statistics: Proceedings of the 16th Symposium on the Interface, Atlanta GA, MarchSpringer-Verlag, Unrefereed articles accepted by editors.  N. Rowe, Antisampling for estimation: an overview.
IEEE Transactions on Software Engineering, SE, 10 (October ), Also technical report NPS, Computer Science Department, Naval Postgraduate Section of a book (refereed):  N. Rowe, Other applications of AI to education. Section IX.D of The Handbook of. Data cubes combine an easy-to-understand conceptual model with an implementation that enables the fast summarization of large data sets.
This makes them a. book ISBN: DOI /APCIP Authors. Close. User assignment Assign yourself or invite other person as author. Synchronization Timing Recovery FFT Rate Estimation signal sampling demodulation.
A fourth application is to "antisampling" techniques for estimating values of statistics on a database . Antisampling is like an opposite of sampling, obtaining its estimates from reasoning about previously computed statistics rather than samples of the data; it can be much faster than sampling when data is kept in secondary storage.
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