This substantial volume has two principal objectives. First it
provides an overview of the statistical foundations of
Simulation-based inference. This includes the summary and synthesis
of the many concepts and results extant in the theoretical
literature, the different classes of problems and estimators, the
asymptotic properties of these estimators, as well as descriptions
of the different simulators in use. Second, the volume provides
empirical and operational examples of SBI methods. Often what is
missing, even in existing applied papers, are operational issues.
Which simulator works best for which problem and why? This volume
will explicitly address the important numerical and computational
issues in SBI which are not covered comprehensively in the existing
literature. Examples of such issues are: comparisons with existing
tractable methods, number of replications needed for robust
results, choice of instruments, simulation noise and bias as well
as efficiency loss in practice.
General
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