Articles Information
International Journal of Materials Chemistry and Physics, Vol.2, No.2, Apr. 2016, Pub. Date: Jan. 18, 2016
An Application of Negative Powers of Poisson Numbers in Crystallography
Pages: 50-53 Views: 2805 Downloads: 1215
Authors
[01]
Julian Henn, Bayreuth, Germany.
Abstract
The formerly introduced negative integer powers [Henn (2012). ActaCryst. A 68, 703--704] are applied to a crystallographic problem. The formal notation is slightly changed in order to simplify and unify the formal appearance. The purpose of this publication is to generalize the formalism from negative integer powers to negative real-valued powers with the help of a generalized hypergeometric function. The application demonstrates that the formalism works successfully. For all powers, the expectation values approach zero for small values of the Poisson parameter , whereas the solutions known from the literature, that all use a truncated and renormalized probability density function, approach one in this case. The truncation of the probability density function from the literature leads to a wrong result in the application.
Keywords
Poisson Distribution, Negative Powers, Hypergeometric Function, Generalized Hypergeometric Function
References
[01]
Blessing, R. H. 1997.Outlier Treatment in Data Merging, Journal of Applied Crystallography 30, 421-426.
[02]
Chao, M.T. and Strawderman, W.E. 1972. Negative moments of positive random variables, Journal of the American Statistical Association 67, 429-431.
[03]
Dremin, I.M.. 1994. Fractional moments of distributions, JETP Letters 59, 585-588.
[04]
Gupta, R.C., 1979.On negative moments of generalized poisson distribution, Series Statistics 10, 169-172.
[05]
Haight, F. 1967. Handbook of the Poisson Distribution (Publications in operations research, Wiley).
[06]
Henn, J. 2012., Expectation valued for integer powers of a Poisson-distributed random number Acta Crystallogr. A68, 703-704.
[07]
Henn, J. and Meindl, K. 2010. Is there a fundamental upper limit for the significance I/(I)) of observations from X-ray and neutron diffraction data?, Acta Crystallogr. A66, 676-684.
[08]
Jones, C., and Zhigljavsky, A.A. 2004. Approximating the negative moments of the Poisson distribution, Statistics and Probability Letters 66, 171-181.
[09]
Phillips, T.R.L. and Zhigljavsky, A. 2014. Approximation of inverse moments of discrete distributions, Statistics & Probability Letters 94, 135-143.
[10]
Schwarzenbach, D. and Abrahams, S. C. and Flack, H. D. and Gonschorek, W. and Hahn, Th. and Huml, K. and Marsh, R. E. and Prince, E. and Robertson, B. E. and Rollett, J. S. and Wilson, A. J. C. 1989. Statistical descriptors in crystallography: Report of the IUCr Subcommittee on Statistical Descriptors, Acta Crystallogr. 45, 63-75.
[11]
Hu, S., Wang, X. and Yang, W., Wang, X. 2014. A Note on the Inverse Moment for the Non Negative Random Variables, Communications in Statistics - Theory and Methods 43, 1750-1757.
[12]
Tiku, M.L. 1964.A Note on the Negative Moments of a Truncated Poisson Variate, Journal of the American Statistical Association 59, 1220-1224.