AIMS and SCOPE | Advances and Applications in Statistics and Probability

AIMS and SCOPES:

The aim of AASP is to publish original and high-quality research articles, reviews, and letters in all areas of statistics and probability. We welcome submissions in the following areas, among others:

Statistical theory: including probability theory, statistical inference, statistical modeling, and experimental design.
Statistical methods: including machine learning, Bayesian statistics, frequentist statistics, statistical computing, and data analysis.
Statistical applications: including econometrics, biostatistics, environmental statistics, social statistics, and big data.
Probability theory and methods: including stochastic processes, random matrices, Markov chains, and Monte Carlo methods.
Probability applications: including finance, insurance, engineering, physics, and biology.

All submissions are subject to a rigorous peer-review process, and we only accept articles that meet our high standards of quality and academic rigor. We also prioritize articles that have a clear and significant impact on the field, and that contribute to advancing our understanding of statistics and probability. AASP is committed to upholding the highest standards of ethical and academic integrity, and all authors are expected to adhere to our Code of Ethics and Code of Conduct.

Some possible keywords or sub-topics related to our aims and scopes are: statistical methodology, statistical software, data mining, statistical genetics, spatial statistics, time series analysis, causal inference, missing data, meta-analysis, Bayesian networks, computational statistics, statistical consulting, statistical education, statistical graphics, model selection, decision theory, statistical optimization, statistical quality control, statistical simulation, survey research, experimental statistics, reliability analysis, nonparametric statistics, multivariate analysis, statistical physics, statistical neuroscience, and statistical ecology.

As an open access journal, AASP is committed to making research freely and immediately available to readers around the world. We adhere to the Budapest Open Access Initiative, which means that all articles published in AASP are licensed under a Creative Commons Attribution License (CC-BY). This license allows readers to freely access, download, share, and reuse the articles, provided that proper attribution is given to the original authors and source of publication. We also provide transparent and detailed information about our article processing charges, which cover the costs of publication and editorial services, and are waived for authors from low- and middle-income countries.

AASP is committed to following best practices and guidelines for open access publishing, as set out by leading organizations such as the Directory of Open Access Journals (DOAJ) and the Committee on Publication Ethics (COPE). We strive to ensure that all articles published in AASP are of the highest quality and integrity, and that our editorial and review processes are transparent, fair, and unbiased. We also encourage authors to share their data and code, and to follow best practices for reproducible research.

In summary, Advances and Applications in Statistics and Probability is a high-quality, open access journal that aims to publish original and impactful research in all areas of statistics and probability. Our mission is to promote innovation and excellence in the field, foster a vibrant community of researchers, and provide a platform for open and transparent communication of research findings. Our vision is to become a leading journal in the field, known for publishing cutting-edge research that has a significant impact on theory, methods, and applications. We welcome submissions from researchers, practitioners, and students around the world, and are committed to upholding the highest standards of ethical and academic rigor.

Keywords/ Subtopics:

Statistical methodology
Probability theory
Statistical software
Data mining
Statistical genetics
Spatial statistics
Time series analysis
Causal inference
Missing data
Meta-analysis
Bayesian networks
Computational statistics
Statistical consulting
Statistical education
Statistical graphics
Model selection
Decision theory
Statistical optimization
Statistical quality control
Statistical simulation
Survey research
Experimental statistics
Reliability analysis
Nonparametric statistics
Multivariate analysis
Statistical physics
Statistical neuroscience
Statistical ecology
Statistical genetics
Statistical inference
Statistical modeling
Statistical computing
Statistical graphics
Statistical learning
Applied statistics
Mathematical statistics
Stochastic processes
Econometrics
Time series econometrics
Financial econometrics
Bayesian statistics
Nonlinear regression
Spatial econometrics
Multilevel modeling
Markov chain Monte Carlo
Survival analysis
Robust statistics
Machine learning
Deep learning
Neural networks
Big data analytics
High-dimensional data analysis
Cluster analysis
Discriminant analysis
Factor analysis
Principal component analysis
Canonical correlation analysis
Correspondence analysis
Data visualization
Data exploration
Hypothesis testing
Confidence intervals
Statistical significance
Type I error
Type II error
Power analysis
Effect size estimation
Goodness of fit tests
Multivariate analysis of variance
Analysis of covariance
Experimental design
Clinical trials
Sample size determination
Survey sampling
Probability distributions
Normal distribution
Binomial distribution
Poisson distribution
Exponential distribution
Gamma distribution
Beta distribution
Chi-square distribution
Student's t-distribution
F-distribution
Copula models
Time-varying parameter models
Structural equation models
Quantile regression
Support vector machines
Bayesian model averaging
Latent variable models
Model-based clustering
Gaussian processes
Hidden Markov models
Decision trees
Random forests
Boosting
Bagging
Ensemble learning
Multiclass classification
Imputation methods
Exploratory factor analysis
Confirmatory factor analysis
Item response theory
Structural equation modeling
Network analysis
Social network analysis
Latent class analysis
Time series forecasting
Seasonal time series
Longitudinal data analysis
Panel data analysis
Generalized linear models
Generalized linear mixed models
Zero-inflated models
Bayesian hierarchical models
Spatial autoregressive models
Spatial point processes
Kernel density estimation
Survival regression
Cox regression
Accelerated failure time models
Multistate models
Dirichlet processes
Stochastic volatility models
Multilevel factor analysis
Structural equation modeling with latent variables
Multinomial logistic regression
Analysis of large-scale data
Functional data analysis
Spectral analysis
Wavelet analysis
Empirical Bayes methods
Multivariate time series analysis
High-dimensional inference
Robustness in statistical modeling
Data-driven decision making
Machine learning algorithms for statistical analysis
Statistical inference for complex networks
Model-based inference for non-standard data structures
Bayesian inference for complex models
Nonparametric regression models
Multivariate nonparametric regression
Nonparametric classification methods
Bayesian optimization
Multi-objective optimization
Sensitivity analysis
Spatial clustering
Spatial interpolation
Spatial prediction
Spatio-temporal modeling
Time series clustering
Quantitative risk assessment
Reliability analysis of complex systems
Statistical process control
Quality control methods for complex data structures
Uncertainty quantification
Statistical physics modeling
Applied probability theory
Statistical learning theory
Big data analytics for decision making
Network analysis for complex systems
Multidimensional scaling
Mixture models
Hidden variable models
Independent component analysis
Multiple imputation
Text mining
Image analysis
Audio analysis
Video analysis
Web analytics
Computational linguistics
Natural language processing
Sentiment analysis
Customer segmentation
Data-driven marketing
Online advertising optimization
Pricing optimization
Supply chain optimization
Inventory optimization
Machine learning for fraud detection
Machine learning for credit scoring
Machine learning for predictive maintenance
Machine learning for recommendation systems
Machine learning for medical diagnosis
Machine learning for drug discovery
Machine learning for image recognition
Machine learning for speech recognition
Machine learning for robotics
Machine learning for autonomous vehicles
Machine learning for renewable energy
Deep reinforcement learning
Generative adversarial networks
Explainable artificial intelligence
Fairness in machine learning
Privacy-preserving machine learning
Federated learning
Transfer learning
Active learning
Semi-supervised learning
Self-supervised learning
Multi-task learning
Unsupervised learning
Artificial intelligence in finance
Artificial intelligence in healthcare
Artificial intelligence in transportation
Artificial intelligence in energy
Artificial intelligence in agriculture
Artificial intelligence in manufacturing
Artificial intelligence in education
Artificial intelligence in cybersecurity
Artificial intelligence in social media analysis
Artificial intelligence in journalism
Artificial intelligence in customer service
Artificial intelligence in gaming
Artificial intelligence in sports analytics
Artificial intelligence in the arts
Natural language processing in finance
Natural language processing in healthcare
Natural language processing in customer service
Natural language processing in social media analysis
Natural language processing in journalism
Natural language processing in legal document analysis
Natural language processing in scientific literature analysis
Natural language processing in the arts
Digital image processing in healthcare
Digital image processing in scientific research
Digital image processing in art conservation
Digital image processing in engineering
Digital image processing in quality control
Digital image processing in security and surveillance
Digital image processing in entertainment
Audio analysis in healthcare
Audio analysis in speech recognition
Audio analysis in music recommendation systems