Aims and Scope of the JAMDA
The Journal of Applied Mathematics & Data Analytics (JAMDA) is a premier, peer-reviewed, international journal dedicated to the advancement and dissemination of original, high-quality research at the intersection of applied mathematics and data analytics. Our primary aim is to provide a unified platform for researchers, academics, and industry practitioners to share innovative methodologies, theoretical developments, and practical applications that bridge the gap between mathematical modeling and data-driven discovery.
JAMDA seeks to foster a dynamic, interdisciplinary community by publishing work that leverages rigorous mathematical principles to solve complex, real-world problems in the era of big data.
JAMDA welcomes submissions of original research articles, comprehensive review papers, and insightful case studies. The scope of the journal is broad and interdisciplinary, encouraging contributions that integrate both foundational and applied aspects of the following areas. Topics of interest include, but are not limited to:
Core Areas in Applied Mathematics:
- Mathematical Analysis and Applications: Scientific Computation and Application of Nonlinear Analysis, Complex Analysis, Fixed Point Theory and Applications.
- Numerical Analysis and Scientific Computing:Numerical Methods for ODEs and PDEs, Computational Linear Algebra, High-performance Computing.
- Mathematical Modeling and Simulation:Modeling of Physical, Biological, Financial and Social Systems.
- Optimization and Operations Research:Linear and Non-linear programming, Convex Optimization, Best proximity Theory, Stochastic Optimization, Control Theory.
- Differential Equations and Dynamical Systems:Theory and Applications of Ordinary and Partial Differential Equations.
- Stochastic Processes and Financial Mathematics:Probability Theory, Random Processes, Mathematical finance, and Risk Analysis.
Core Areas of Economics and Financial Mathematics
- Financial Mathematics & Computational Finance: Stochastic calculus & SDEs (Itô’s lemma, diffusion processes), derivative pricing models (Black–Scholes, HJM, local‑volatility, LIBOR market), numerical methods (finite‑difference, lattice, Monte Carlo simulation, PDE solvers), portfolio optimization & risk measures (mean–variance, CVaR, utility‑based), interest‑rate modeling (Vasicek, Hull–White, CIR, multifactor), credit‐risk & default models (Merton, reduced‑form, copula), actuarial mathematics & risk theory (life tables, ruin theory), algorithmic trading & quantitative strategies, computational techniques (parallel/GPU simulation, machine‑learning in finance)
- Econometric Theory & Methods: Identification strategies, endogeneity correction, asymptotic theory, limited‑dependent models, sample‑selection & treatment‑effect models
- Time‑Series Econometrics: ARIMA, ARCH/GARCH family (EGARCH, TGARCH), unit‑root & structural‑break tests, cointegration & error‑correction models, regime‑switching & Markov‑switching models
- Panel Data Econometrics: Fixed & random effects, dynamic panels (Arellano–Bond, System GMM), nonlinear panel models, panel threshold estimation, interactive‑fixed‑effects / factor‑augmented panels
- Microeconometrics & Causal Inference: Instrumental‑variables designs, regression‑discontinuity (sharp & fuzzy), propensity‑score matching & weighting, difference‑in‑differences, synthetic‑control methods
- Nonparametric & Semiparametric Econometrics: Kernel & local‑polynomial regression, series/sieve methods (splines, wavelets), partially linear & varying‑coefficient models, nonparametric IV estimators, semiparametric treatment‑effect estimators
- Bayesian Econometrics & Structural Models: MCMC (Gibbs, Metropolis–Hastings, HMC), variational Bayes & ABC, Bayesian VAR & dynamic factor models, state‑space & stochastic‑volatility estimation, model comparison & Bayesian model averaging, Bayesian DSGE & dynamic discrete‑choice estimation
Core Areas in Data Analytics and Artificial Intelligence:
- Machine Learning and Deep Learning:Supervised, Unsupervised, and Reinforcement Learning; Neural Networks; Algorithm Development.
- Statistical Modeling and Inference:Computational statistics, Bayesian Methods, High-dimensional Statistics, Time-series Analysis.
- Data Mining and Pattern Recognition:Knowledge Discovery in Databases, Clustering, Classification, Anomaly Detection.
- Big Data Analytics:Scalable Algorithms, Data Infrastructure, Stream Data Processing.
- Data Visualization and Predictive Analytics:Techniques for Visual Representation and Forecasting.
Interdisciplinary and Bridge Topics (Special Focus):
JAMDA has a special interest in papers that lie at the nexus of these two fields, such as:
- Data-Driven Modeling and Simulation
- Mathematical Foundations of Machine Learning
- Optimization Methods for Large-Scale Data Problems
- Numerical Algorithms for Data Science
- Integration of Mathematical Models with Real-Time Data
Application Domains:
We encourage submissions that demonstrate the practical impact of these methods in areas such as:
- Engineering and Industrial Systems
- Bioinformatics and Computational Biology
- Healthcare and Medical Analytics
- Economics and Finance
- Environmental Science and Geosciences
- Social Sciences and Network Analysis
