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Journal of Statistics and Computer Science is a double-blind peer reviewed bi-annual journal of the theoretical and applied areas in Statistics and Computer Science. It aims to provide a platform to the researchers working in the theoretical and applied aspects of Statistics and Computer Science and welcomes the highly innovative research papers and review articles. The emphasis is on the theoretical and methodological papers containing substantial derivations and applications. Topics of interest include Theoretical, Methodological, Computational and Applied areas in Statistics and Computer Science. 

Part A: Statistics. Papers providing important original contributions to theoretical and methodological statistics inspired by applications are considered. Papers dealing directly or indirectly, with computational and technical elements are particularly encouraged. These cover developments concerning issues of high-dimensionality, re-sampling, dependence, robustness, filtering, and, in general, the interaction of mathematical and statistical methods, numerical implementations and the extra burden of analyzing large and/or complex datasets in different areas such as agriculture, medicine, epidemiology, biology, psychology, climatology and communication, engineering, ecology, education, operations research, business, economics, management and finance. Innovative algorithmic developments are also of interest, as are the computer programs and the computational environments that implement them as a complement. 

Part B: Computer Science. Emphasis is given to theoretical and applied papers containing substantial derivations or showing potential of a significant impact in the broad area of Computer Science. Topics of interest include artificial intelligence, machine learning, data mining, computer networks, computer security, software engineering, algorithm & complexity, cryptography, computer architecture, databases, operating systems, computational biology and bioinformatics. Interest is focused as well on well-founded studies that demonstrate the practicality of new approaches and procedures. Such studies should involve the rigorous application of statistical techniques, including estimation, inference, and design of experiments. Innovative contributions that use advanced statistical methods are encouraged. The results of the submissions should be replicable. Applications consisting only of routine calculations are not of interest to the journal.