Parallel Algorithms for Linear Models provides a complete and detailed
account of the design, analysis and implementation of parallel
algorithms for solving large-scale linear models. It investigates and
presents efficient, numerically stable algorithms for computing the
least-squares estimators and other quantities of interest on massively
parallel systems.
The monograph is in two parts. The first part consists of four chapters
and deals with the computational aspects for solving linear models that
have applicability in diverse areas. The remaining two chapters form the
second part, which concentrates on numerical and computational methods
for solving various problems associated with seemingly unrelated
regression equations (SURE) and simultaneous equations models.
The practical issues of the parallel algorithms and the theoretical
aspects of the numerical methods will be of interest to a broad range of
researchers working in the areas of numerical and computational methods
in statistics and econometrics, parallel numerical algorithms, parallel
computing and numerical linear algebra. The aim of this monograph is to
promote research in the interface of econometrics, computational
statistics, numerical linear algebra and parallelism.