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Algorithm 885: Computing the Logarithm of the Normal Distribution
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ACM Transactions on Mathematical Software (TOMS) archive
Volume 35 ,  Issue 3  (October 2008) table of contents
Article No. 20  
Year of Publication: 2008
ISSN:0098-3500
Author
Jean Marie Linhart  StataCorp LP
Publisher
ACM  New York, NY, USA
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APPENDICES and SUPPLEMENTS
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Software for Computing the Logarithm of the Normal Distribution


ABSTRACT

We present and compare three C functions to compute the logarithm of the cumulative standard normal distribution. The first is a new algorithm derived from Algorithm 304’s calculation of the standard normal distribution via a series or continued fraction approximation, and it is good to the accuracy of the machine. The second is based on Algorithm 715’s calculation of the standard normal distribution via rational Chebyshev approximation. This is related to, and an improvement on, the algorithm for the logarithm of the normal distribution available in the software package R. The third is a new and simple algorithm that uses the compiler’s implementation of the error function, and complement of the error function, to compute the log of the normal distribution.


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
Abramowitz, M. and Stegun, I., Eds. 1972. Handbook of Mathematical Functions. Dover, New York, NY.
 
2
Adams, A. G. 1969a. Algorithm 39: Areas under the normal curve. Comput. J. 12, 197--198.
3
 
4
Brophy, A. L. and Wood, D. L. 1989. Algorithms for fast and precise computation of the normal integral. Behav. Res. Meth. Instr. Comput. 21, 447--454.
 
5
Clenshaw, C., Ed. 1962. Chebyshev Series for Mathematical Functions. National Physical Laboratory Mathematical Tables, volume 5. Her Majesty’s Stationery Office, London.
 
6
Cody, W. J. 1969. Rational Chebyshev approximations for the error function. Math. Computation 23, 631--637.
7
 
8
Cooper, B. E. 1968. Algorithm AS2: the normal integral. Appl. Statis. 17, 186--188.
 
9
Fletcher, A. and Rosenhead, L. 1962. Index of Mathematical Tables, 2nd ed. Scientific Computing Service Limited, London.
 
10
 
11
Hill, I. D. 1969. Remark on Algorithm AS2. Appl. Statis. 18, 299--300.
 
12
Hill, I. D. 1973. Algorithm AS66: the normal integral. Appl. Statis. 22, 424--427.
13
14
15
16
 
17
IEEE. 1985. IEEE Standard 754-1985 for Binary Floating-Point Arithmetic. The Institute of Electrical and Electronics Engineers, Inc., New York, NY.
 
18
Marsaglia, G. 2004. Evaluating the normal distribution. J. Statis. Softw. 11, 1--7.
 
19
Martynov, G. V. 1981. Evaluation of the normal distribution function. J. Sov. Math. 17, 1857--1875.
 
20
Monahan, J. 1981. Approximating the log of the normal cumulative. In Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface. Springer Verlag, New York, NY, 304--307.
 
21
Pearson, E. S. and Hartley, H. O., Eds. 1954. Biometrika Tables for Statisticians. Vol. 1. Cambridge University Press, Cambridge, UK.
 
22
Press, W. H. et al. 1992. Numerical Recipes in C, 2 ed. Cambridge University Press, Cambridge, UK.
 
23
R Development Core Team. 2007. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. ISBN (3-900051-07-0).
 
24
Schonfelder, J. L. 1978. Chebyshev expansions for the error and related functions. Math. Computation 32, 144, 1232--1240.
 
25
Sheppard, W. F., Ed. 1939. The Probability Integral. British Association for the Advancement of Science, Mathematical Tables, vol. 7. Cambridge, University Press, Cambridge, UK.
 
26
Zeileis, A. and Kleiber, C. 2005. Validating multiple structural change models --- a case study. J. Appl. Econometrics 20, 685--690.