Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2002.9B.3.271

Computation of Noncentral F Probabilities using multilayer neural network  

Gu, Sun-Hee (Jeonju University)
Abstract
The test statistic in ANOVA tests has a single or doubly noncentral F distribution and the noncentral F distribution is applied to the calculation of the power functions of tests of general linear hypotheses. Although various approximations of noncentral F distribution are suggested, they are troublesome to compute. In this paper, the calculation of noncentral F distribution is applied to the neural network theory, to solve the computation problem. The neural network consists of the multi-layer perceptron structure and learning process has the algorithm of the backpropagation. Using fables and figs, comparisons are made between the results obtained by neural network theory and the Patnaik's values. Regarding of accuracy and calculation, the results by neural network are efficient than the Patnaik's values.
Keywords
Neural Network; Noncentral F Distribution; Patnaik's Values;
Citations & Related Records
연도 인용수 순위
  • Reference
1 P.J. Bickel and K.A. Doksum, 'Mathematical Statistics : Basic ideas and selected topics,' SanFrancisco : HoldenDay, 1977
2 B. Cheng and D.M. Titterington, 'Neural Networks : A review from a statistical perspective,' Statistical Science, Vol.9, pp.2-54, 1994   DOI   ScienceOn
3 김대수, '신경망 이론과 응용 (I),(II),' 하이테크정보, 1994
4 R. Chattamvelli, 'On the doubly non-central F distribution,' Computational Statistics and Data Analysis, Vol.20, pp.481-489, 1995   DOI   ScienceOn
5 L.K. Jones, 'A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training,' The Annals of Statistics, Vol.20, pp.608-613, 1992   DOI   ScienceOn
6 H. John and K. Anders and G.P. Richard, 'Introduction to the Theory Neural Computation,' Addison Wesley Publishing Co. Inc, 1991
7 R.A. Fisher, 'The general sampling distribution of the multiple correlation coefficient,' Proceedings of the Royal Society of London, Series A, Vol.1, pp.654-673, 1928
8 N.L. Johnson, 'On an extension of the connexion between the Poisson and ${\chi}^2$ distributions,' Biometrika, Vol.46, pp.352-363, 1993   DOI
9 N.L. Johnson and S. Kotz, 'Continuous Univariate Distributions,' Boston : Houghton Mifflin, 1970
10 J.N. Hwang and H. Li. M. Maechler and D. Martin and J. Schimert, 'Regression modeling in backpropaga-tion and projection pursuit learning,' IEEE transactions on neural networks, Vol.5, pp.342-353, 1994   DOI   ScienceOn
11 P.B. Patnaik, 'The noncentral ${\chi}^2$ and F-distributions and their applications,' Biometrika, Vol.36, pp.202-232, 1949   DOI
12 D.F. Specht, 'A general regression neural network,' IEEE transactions on neural networks, Vol.2, pp.568-576, 1991   DOI   ScienceOn
13 R. Price, 'Some noncentral F-distributions expressed in closed form,' Biometrika, Vol.51, pp.107-122, 1964   DOI
14 D.E. Rumelhart and E.H. Geoffrey and J.W. Ronald, 'Learning representations by backpropagating errors,' Nature, Vol.323, pp.533-536, 1986   DOI
15 G.A.F. Seber, 'The noncentral chi-squared and beta distributions,' Biometrika, Vol.50, pp.542-544, 1963   DOI
16 M. Sibuya, 'On the noncentral beta distribution function,' Unpublished manuscript, 1967
17 H. White, 'Learning in artificial net-works : A Statistical perspective,' Neural Computation, Vol.1(4), pp.425-469, 1989   DOI
18 M. Simth, 'Neural Networks for Statistical Modeling,' New York : Van Nostrand Reinhold
19 P.C. Tang, 'The power function of the analysis of variance tests with tables and illustrations of their use,' Statistical Research Memoirs, Vol.2, pp.126-150, 1938
20 M.L. Tiku, 'A note on approximating to the noncentral F-distribution,' Biometrika, Vol.53, pp.606-610, 1975
21 H. White, 'Connectionist nonpara-metric regression : multilayer feed-forward networks can learn arbitrary mappings,' Neural Networks, Vol.3, pp.535-549, 1990   DOI   ScienceOn