The aim of this issue is to promote the novel ideas for efficient optimization of statistical decisions and predictive inferences under parametric uncertainty of underlying models with applications. It is expected that these ideas give interesting and novel contributions to statistical theory and its applications at a good mathematical level, where the theoretical results are obtained via the frequentist (non-Bayesian) statistical approach. Frequentist probability interpretations of the methods considered are clear. Bayesian methods are not considered here. It will be noted, however, that although subjective Bayesian approach has a clear personal probability interpretation, it is not generally clear how this should be applied to non-personal prediction or decisions. Objective Bayesian methods, on the other hand, do not have clear probability interpretations in finite samples. Since genuinely useful applications remain rare, this issue focuses on the practice of applying the ideas presented here to solve efficiently real problems with numerical results on the relative efficiency of the proposed method (as compared with the known methods) and examples for the applicability of the theoretical results. It is assumed that the efficient optimization take into account statistical information, which is contained in the past, previous, or current data samples, as completely as possible to allow one to find efficient decision rules and predictive inferences. This special issue has to provide academicians and young researchers worldwide high quality peer-reviewed research articles, covering the topics of primary interest, and to bring together mathematicians’ papers from different aspects of efficient optimization of statistical decisions and predictive inferences (under parametric uncertainty of underlying models with applications) as well as to present different points of views and methods.

The topics covered by the special issue include (but are not limited to):

1. Diagnostics
2. Signal Processing
3. Transportation Processes
4. Dual Control
5. Pattern Recognition
6. Reliability
7. Quality Control
8. Inventory Control
9. Industrial Engineering
10. Planning In-Service Inspections
11. Acceptance Testing
12. Prediction
13. Statistical Decisions in Medicine
14. Statistical Decisions in Remote Sensing