Journals in Statistics

Computational Statistics & Data Analysis

ISSN: 0167-9473
5 Year impact factor: 1.7
Impact factor: 1.5

The Official Journal of the Network Computational and Methodological Statistics (CMStatistics) and the International Association of Statistical Computing (IASC)Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for 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 four refereed sections which are divided into the following subject areas:I) Computational Statistics - Manuscripts dealing with:the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics, 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), 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, 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 (social sciences, marketing, psychometrics, chemometrics, signal processing, medical statistics, environmentrics, statistical physics).IV) Statistical Data Science - The manuscripts concern with well-founded theoretical and applied data-driven research, with a significant computational or statistical methodological component for data analytics. Emphasis is given to comprehensive and reproducible research, including data-driven methodology, algorithms and software. This journal section serves as a complementary component to the network Computational and Methodological Statistics (CMStatistics).

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International Journal of Forecasting

ISSN: 0169-2070
5 Year impact factor: 6.8
Impact factor: 6.9

Official Publication of the International Institute of ForecastersThe International Journal of Forecasting is the leading journal in its field. It is the official publication of the International Institute of Forecasters (IIF) and shares its aims and scope. More information about the IIF may be found at https://www.forecasters.org.The International Journal of Forecasting publishes high quality refereed papers covering all aspects of forecasting. Its objective (and that of the IIF) is to unify the field, and to bridge the gap between theory and practice, making forecasting useful and relevant for decision and policy makers. The journal places strong emphasis on empirical studies, evaluation activities, implementation research and ways of improving the practice of forecasting. It is open to many points of view and encourages debate to find solutions for problems facing the field.Topics covered in the International Journal of Forecasting:• Economic and econometric forecasting • Marketing forecasting • New products forecasting • Financial forecasting • Production forecasting • Technological forecasting • Forecasting applications in business, government, and the military • Demographic forecasting • Energy forecasting • Climate forecasting • Crime forecasting • Seasonal adjustments and forecasting • Time series forecasting • Legal and political aspects of forecasting • Implementation of forecasting • Judgmental/psychological aspects of forecasting • Impact of forecast uncertainty on decision making • Organizational aspects of forecasting • Sport forecasting • Machine Learning forecasting • Forecasting methodology • Election forecasting • Big data forecasting Features of the IJF include research papers, research notes, discussion articles, book reviews, editorials and letters.Data and computer programs associated with articles published in the International Journal of Forecasting are provided as online supplements on ScienceDirect.ObjectivityTo ensure fairness and objectivity, double-blind reviewing will be used.Replication studiesThe IJF encourages replication studies, especially of highly cited papers. See Encouraging replication and reproducible research (an editorial published in 2010) for further information. A replication study that confirms that a published paper can be successfully replicated would normally be quite short (about a page is often sufficient to describe what calculations and comparisons have been done). Where a previously published paper has not been successfully replicated, more details are required to explain how the results differ from those previously published.

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Journal of Statistical Planning and Inference

ISSN: 0378-3758
5 Year impact factor: 0.9
Impact factor: 0.8

The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists, such as clustering, post model selection inference, deep learning and random networks.We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.We want to serve as the broadest international platform for high quality research on every aspect of our field, traditional and cutting edge. The quality and the breadth of our editorial board reflects that singular priority.

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Physica A: Statistical Mechanics and its Applications

ISSN: 0378-4371
5 Year impact factor: 2.6
Impact factor: 2.8

Recognized by the European Physical SocietyPhysica A: Statistical Mechanics and its Applications publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems, or the large scale, by studying the statistical properties of the microscopic or nanoscopic constituents. Applications of the concepts and techniques of statistical mechanics include: applications to physical and physiochemical systems such as solids, liquids and gases, interfaces, glasses, colloids, complex fluids, polymers, complex networks, applications to economic and social systems (e.g. socio-economic networks, financial time series, agent based models, systemic risk, market dynamics, computational social science, science of science, evolutionary game theory, cultural and political complexity), and traffic and transportation (e.g. vehicular traffic, pedestrian and evacuation dynamics, network traffic, swarms and other forms of collective transport in biology, models of intracellular transport, self-driven particles), as well as biological systems (biological signalling and noise, biological fluctuations, cellular systems and biophysics); and other interdisciplinary applications such as artificial intelligence (e.g. deep learning, genetic algorithms or links between theory of information and thermodynamics/statistical physics.).Physica A does not publish research on mathematics (e.g. statistics) or mathematical methods (e.g. solving differential equations) unless an original application to a statistical physics problem is included. Also research on fluid mechanics intended for an engineering readership as well as ordinary economic/econometrical studies falls outside the scope of Physica A .Specific subfields covered by the journal are statistical mechanics applications to:Soft matterBiological systems and systems biologyChemical systemsEconophysics and sociophysicsTraffic and transportationPhase transitionsComplex systemsDeep learning, genetic algorithms and other methods of AIBenefits to authors We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our Support Center

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Statistics & Probability Letters

ISSN: 0167-7152
5 Year impact factor: 0.8
Impact factor: 0.9

Statistics & Probability Letters adopts a novel and highly innovative approach to the publication of research findings in statistics and probability. It features concise articles, rapid publication and broad coverage of the statistics and probability literature.Statistics & Probability Letters is a refereed journal. Articles will be limited to six journal pages (13 double-space typed pages) including references and figures. Apart from the six-page limitation, originality, quality and clarity will be the criteria for choosing the material to be published in Statistics & Probability Letters. Every attempt will be made to provide the first review of a submitted manuscript within three months of submission.The proliferation of literature and long publication delays have made it difficult for researchers and practitioners to keep up with new developments outside of, or even within, their specialization. The aim of Statistics & Probability Letters is to help to alleviate this problem. Concise communications (letters) allow readers to quickly and easily digest large amounts of material and to stay up-to-date with developments in all areas of statistics and probability.The mainstream of Letters will focus on new statistical methods, theoretical results, and innovative applications of statistics and probability to other scientific disciplines. Key results and central ideas must be presented in a clear and concise manner. These results may be part of a larger study that the author will submit at a later time as a full length paper to SPL or to another journal. Theory and methodology may be published with proofs omitted, or only sketched, but only if sufficient support material is provided so that the findings can be verified. Empirical and computational results that are of significant value will be published. We also plan to publish applications and case studies that demonstrate a novel use of existing techniques or have interesting innovative ideas about data collection, modelling or inference.

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Stochastic Processes and their Applications

ISSN: 0304-4149
5 Year impact factor: 1.4
Impact factor: 1.1

An official journal of the Bernoulli Society for Mathematical Statistics and Probability.Stochastic Processes and their Applications is a mathematics journal that publishes papers on the theory and applications of stochastic processes.It is concerned with the following: concepts and techniquesmathematically challenging questions in sciences and engineering.characterization,structural properties,limit theoremsinferencecontrol of stochastic processes.The journal is exacting and scholarly in its standards. Every effort is made to promote innovation, vitality, and communication between disciplines. Submissions are anonymously peer-reviewed.This journal has an Open Archive. All published items, including research articles, have unrestricted access and will remain permanently free to read and download 48 months after publication. All papers in the Archive are subject to Elsevier's user license.If you require any further information or help, please visit our Support Center.

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Journals in Statistics

Computational Statistics & Data Analysis

International Journal of Forecasting

Journal of Statistical Planning and Inference

Physica A: Statistical Mechanics and its Applications

Statistics & Probability Letters

Stochastic Processes and their Applications