ABSTRACT
Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Statistical computing traditionally has more emphasis on numerical methods and algorithms, such as optimization and random number generation, while computational statistics may encompass such topics as exploratory data analysis, Monte Carlo methods, and data partitioning, etc. However, most researchers who apply computationally intensive methods in statistics use both computational statistics and statistical computing methods; there is much overlap and the terms are used differently in different contexts and disciplines. Gentle [113] and Givens and Hoeting [121] use “computational statistics” to encompass all the relevant topics that should be covered in a modern introductory text, so that “statistical computing” is somewhat absorbed under this more broad definition of computational statistics. On the other hand, journals and professional organizations seem to use both terms to cover similar areas. Some examples are the International Association for Statistical Computing (IASC), part of the International Statistical Insititute, and the Statistical Computing section of the American Statistical Association.
