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About the journal
Mathematical Statistics and Learning is an open-access journal published by EMS Press in Germany, publishing in english. It is listed in DOAJ according to the sources read. Declared review time at DOAJ: 80 weeks. Facts are reproduced as recorded at their sources with their capture dates; PublishLens indicators are available after sign-in.
Basic facts
- Publisher
- EMS Press
- ISSN
- 2520-2316 · 2520-2324
- Country
- Germany
- Languages
- english
- Access
- Open access
- Articles in DOAJ
- 0
- Declared review time
- 80 weeks
- Declared peer-review model
- other
- Official website
- https://ems.press/journals/msl
Index listings
- DOAJ · 2023-04-30
- Indexing
- DOAJ
- APC
- —
- Declared review time (DOAJ)
- 80 weeks — journal-declared only (not measured)
- Activity (OpenAlex)
- Active — latest works 2026
- PublishLens list status
- Shown after sign-in
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Methodology signals (dated facts from their sources)
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Descriptive metadata from public sources (DOAJ, OpenAlex, PubMed) as of the capture date shown — contains no assessment or judgment.
Articles in DOAJ
0
Average review time (journal-declared via DOAJ)
80weeks
Works in OpenAlex
57
Last recorded publication year
2025
Reported by OpenAlex in 2 of 3 sources (CC0 flags, not verified indexing)DOAJSciELOOpenAlex Core
h-index
11
i10-index
14
From OpenAlex open data (CC0).
Editorial & policy10
- Last DOAJ review date
- 2025-03-07
- Added to DOAJ
- 2023-04-30
- Declared plagiarism screening
- Not declared
- Other fees beyond APC
- No
- Declared digital preservation
- CLOCKSS
- Observed digital preservation (Keepers registry)— snapshot 2026-10-05
- CLOCKSS
- Editorial board
- Official page ↗
- Open references deposited at Crossref
- 0%
- ORCID iDs in current deposits
- 100%
- Peer-review policy
- View policy
Identity & continuity1
- Source type
- journal
Classification2
Subjects (DOAJ)
Science: Mathematics: Probabilities. Mathematical statistics
LCC codes
QA273-280
Subject areas
- Statistical Methods and Inference
- Statistical Methods and Inference
- Statistical Methods and Inference
- Stochastic Gradient Optimization Techniques
- Probability and Statistical Research
- Sparse and Compressive Sensing Techniques
- Sparse and Compressive Sensing Techniques
- Machine Learning and Algorithms
Show all subjects (75)
- Sparse and Compressive Sensing Techniques
- Machine Learning and Algorithms
- Complex Network Analysis Techniques
- Bayesian Methods and Mixture Models
- Bayesian Methods and Mixture Models
- Markov Chains and Monte Carlo Methods
- Bayesian Methods and Mixture Models
- Complexity and Algorithms in Graphs
- Random Matrices and Applications
- Random Matrices and Applications
- Machine Learning and Algorithms
- Neural Networks and Applications
- Advanced Bandit Algorithms Research
- Random Matrices and Applications
- Advanced Statistical Methods and Models
- Statistical Methods and Applications
- Numerical methods in inverse problems
- Advanced Statistical Methods and Models
- Complex Network Analysis Techniques
- Neural Networks and Applications
- Complexity and Algorithms in Graphs
- Algorithms and Data Compression
- Markov Chains and Monte Carlo Methods
- Numerical methods in inverse problems
- Complex Network Analysis Techniques
- Model Reduction and Neural Networks
- Gaussian Processes and Bayesian Inference
- Distributed Sensor Networks and Detection Algorithms
- Algorithms and Data Compression
- Complexity and Algorithms in Graphs
- Neural Networks and Applications
- Optimization and Search Problems
- Limits and Structures in Graph Theory
- Geometric Analysis and Curvature Flows
- Markov Chains and Monte Carlo Methods
- Numerical methods in inverse problems
- Advanced Statistical Methods and Models
- Model Reduction and Neural Networks
- Distributed Sensor Networks and Detection Algorithms
- Matrix Theory and Algorithms
- Complex Systems and Time Series Analysis
- Fault Detection and Control Systems
- Stochastic Gradient Optimization Techniques
- Stochastic processes and statistical mechanics
- Stochastic processes and statistical mechanics
- Spectral Theory in Mathematical Physics
- Stochastic processes and statistical mechanics
- Advanced Optimization Algorithms Research
- Mathematical Dynamics and Fractals
- Mathematical Dynamics and Fractals
- Fault Detection and Control Systems
- Distributed systems and fault tolerance
- Generative Adversarial Networks and Image Synthesis
- Complex Systems and Time Series Analysis
- Matrix Theory and Algorithms
- Advanced Graph Theory Research
- Face and Expression Recognition
- Stochastic Gradient Optimization Techniques
- Reinforcement Learning in Robotics
- Constraint Satisfaction and Optimization
- Error Correcting Code Techniques
- Point processes and geometric inequalities
- Face and Expression Recognition
- Advanced Graph Theory Research
- Reinforcement Learning in Robotics
- Distributed systems and fault tolerance
- Generative Adversarial Networks and Image Synthesis
Keywords (DOAJ)
- mathematical statistics
- machine learning
- theoretical computer science
- signal processing
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