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Statistical Methods in Data Science and Applications
This special issue belongs to the section “D1: Probability and Statistics“.
Special Issue Information
Dear Colleagues,
With the development of big data, the importance of data science is growing rapidly, leading to a large number of studies in various disciplines. such as mathematics, statistics, computer science, and artificial intelligence. Data science requires modeling, computation, and learning to realize conversion from data to information, from information to knowledge, and from knowledge to decision making. Due to the complexity of big data, such as missing, high- and ultrahigh-dimensional, response-dependent, time series, distributed storage, etc., the existing big data analysis theories, methods, and algorithms are facing huge challenges, particularly in terms of basic statistical theories, methods, and algorithms around estimation, hypothesis testing, confidence interval, and variable selection, including frequentist and Bayesian big data.
Prof. Dr. Niansheng Tang
Prof. Dr. Shen-Ming Lee
Guest Editors
Manuscript Submission Information
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Keywords
- big data
- missing data
- high-dimensional data
- ultrahigh-dimensional data
- response-dependent data
- survival data
- longitudinal data
- censored data
- time series data
- machine learning
- meta learning
- deep learning
- online learning
- reinforcement learning
- knowledge graph
- distributed inference
- Bayesian inference
- variational Bayesian learning
- supervised learning
- semi-supervised learning
- resampling-based inference
- estimation
- variable selection
- hypothesis test
- confidence interval
- computation
- optimization algorithm
- data clustering
- data classification
- data mining
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Published Papers
Mathematics - ISSN 2227-7390