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Generation of random numbers using shape preserving quadratic splines
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Source ACM Southeast Regional Conference archive
Proceedings of the 16th annual Southeast regional conference table of contents
Atlanta, Georgia
Pages: 216 - 218  
Year of Publication: 1978
Authors
D. F. McAllister  N.C. State University
J. A. Roulier  N.C. State University
M. Evans  N.C. State University
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 12,   Citation Count: 2
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ABSTRACT

Let F be an arbitrary continuous cumulative distribution function of a single variable specified by a finite set of points. A (smooth) increasing quadratic spline is constructed which interpolates the data points and preserves the convexity of the data [2]. The spline is compared with Akima's piecewise cubic approximation [1] for several common distributions F.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
L. E. Deimel, D. F. McAllister and J. A. Roulier, "Smooth Curve Fitting with Shape Preservation Using Osculatory Quadratic Splines", Proceedings of the Computer Science and Statistics: Eleventh Annual Symposium on the Interface, 1978, pp. 343-347.
 
3
V. O. Guerra, R. A. Tapia and J. R. Thompson, "A Random Number Generator for Continuous Random Variables Based on an Interpolation Procedure of Akima", ICSA Technical Report, Rice University, 1972.

Collaborative Colleagues:
D. F. McAllister: colleagues
J. A. Roulier: colleagues
M. Evans: colleagues