Efficient procedure to generate generalized Gaussian noise using linear spline tools
Author(s) -
Abdelilah Monir,
H. Mraoui,
Abdeljabbar El Hilali
Publication year - 2020
Publication title -
boletim da sociedade paranaense de matemática
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.347
H-Index - 15
eISSN - 2175-1188
pISSN - 0037-8712
DOI - 10.5269/bspm.40837
Subject(s) - univariate , generalized inverse gaussian distribution , mathematics , piecewise , inverse gaussian distribution , gaussian , inverse , simple (philosophy) , bivariate analysis , spline (mechanical) , piecewise linear function , generalized inverse , algorithm , mathematical optimization , gaussian process , distribution (mathematics) , mathematical analysis , multivariate statistics , statistics , gaussian random field , philosophy , physics , geometry , epistemology , structural engineering , quantum mechanics , engineering
In this paper, we propose a simple method to generate generalized Gaussian noises using the inverse transform of cumulative distribution. This inverse is expressible by means of the inverse incomplete Gamma function. Since the implementation of Newton’s method is rather simple, for approximating inverse incomplete Gamma function, we propose a better and new initial value exploiting the close relationship between the incomplete Gamma function and its piecewise linear interpolant. The numerical results highlight that the proposed method simulates well the univariate and bivariate generalized Gaussian noises.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom