Description

The routines include:

• generator of independent and identically distributed random vectors from the truncated univariate and multivariate distributions;

• (Quasi-) Monte Carlo estimator and a deterministic upper bound of the cumulative distribution function of the multivariate normal and Student distributions;

• algorithm for the accurate computation of the quantile function of the normal distribution in the extremes of its tails.

Author(s)

Leo Belzile and Z. I. Botev, email: botev@unsw.edu.au and web page: https://web.maths.unsw.edu.au/~zdravkobotev/

References

• Z. I. Botev (2017), The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting, Journal of the Royal Statistical Society, Series B, 79 (1), pp. 1–24.

• Z. I. Botev and P. L'Ecuyer (2015), Efficient Estimation and Simulation of the Truncated Multivariate Student-t Distribution, Proceedings of the 2015 Winter Simulation Conference, Huntington Beach, CA, USA

• Gibson G. J., Glasbey C. A., Elston D. A. (1994), Monte Carlo evaluation of multivariate normal integrals and sensitivity to variate ordering, In: Advances in Numerical Methods and Applications, pages 120–126