Journal Guide

CMFT is an international mathematics journal which publishes carefully selected original research papers in complex analysis (in a broad sense), and on applications or computational methods related to complex analysis. Survey articles of high standard and current interest can be considered for publication as well. Contributed papers should be written in English (exceptions in rare cases are tolerated), and in a lucid, expository style. Papers should not exceed 30 printed pages.

This journal publishes research on the analysis and development of computational algorithms and modeling technology for optimization. It examines algorithms either for general classes of optimization problems or for more specific applied problems, stochastic algorithms as well as deterministic algorithms. Computational Optimization and Applications covers a wide range of topics in optimization, including: large scale optimization, unconstrained optimization, constrained optimization, nondifferentiable optimization, combinatorial optimization, stochastic optimization, multiobjective optimization, and network optimization. It also covers linear programming, complexity theory, automatic differentiation, approximations and error analysis, parametric programming and sensitivity analysis, management science, and more. This peer-reviewed journal features both research and tutorial papers that provide theoretical analysis, along with carefully designed computational experiments.Officially cited as: Comput Optim Appl

Computational Psychiatry publishes original research articles and reviews that involve the application, analysis, or invention of theoretical, computational and statistical approaches to mental function and dysfunction. Topics include brain and behavioral modeling over multiple scales and levels of analysis, and the use of these models to understand psychiatric dysfunction, its remediation, its relation to social or biological factors, and the development and sustenance of healthy cognition throughout the lifespan.

Social networks, Social computing, Mathematics of social networks, Computational aspects of social networks, Therory of social computing

Computational Statistics (CompStat) is an international journal that fosters the publication of applications and methodological research in the field of computational statistics. The journal provides a forum for computer scientists, mathematicians, and statisticians working in a variety of areas in statistics, including biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge-based systems, and Bayesian computing. CompStat papers emphasize the contribution to and influence of computing on statistics and vice versa. The journal also publishes hardware, software, and package reports as well as book reviews.Officially cited as: Comput Stat

Computational Statistics & Data Analysis (CSDA), the official journal of the International Association of Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of three refereed sections, and a fourth section dedicated to news on statistical computing. The refereed sections are divided into the following subject areas:I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics, computational econometrics, computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article.II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, econometrics, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures.Statistical methodology includes, but not limited to: bootstrapping, classification techniques, clinical trials, data exploration, density estimation, design of experiments, pattern recognition/image analysis, parametric and nonparametric methods, statistical genetics, Bayesian modeling, outlier detection, robust procedures, cross-validation, functional data, fuzzy statistical analysis, mixture models, model selection and assessment, nonlinear models, partial least squares, latent variable models, structural equation models, supervised learning, signal extraction and filtering, time-series modelling, longitudinal analysis, multilevel analysis and quality control.III) Special Applications - Manuscripts at the interface of statistics and computing (e.g., comparison of statistical methodologies, computer-assisted instruction for statistics, simulation experiments). Advanced statistical analysis with real applications (economics, social sciences, marketing, psychometrics, chemometrics, signal processing, finance, medical statistics, environmentrics, statistical physics).

Computational & Applied Mathematics began to be published in 1981. This journal was conceived as the main scientific publication of SBMAC (Brazilian Society of Computational and Applied Mathematics).   The objective of the journal is the publication of original research in Applied and Computational Mathematics, with interfaces in Physics, Engineering, Chemistry, Biology, Operations Research, Statistics, Social Sciences and Economy. The journal has the usual quality standards of scientific international journals and we aim high level of contributions in terms of originality, depth and relevance. CAM is currently reviewed in Mathematical Reviews and Institute of Scientific Information (Webofscience).