Annual Review of Statistics and Its Application

Annual Review of Statistics and Its Application

ISSN: 2326-8298
eISSN: 2326-831X

AIMS AND SCOPE OF JOURNAL: The Annual Review of Statistics and Its Application informs statisticians, and users of statistics about major methodological advances and the computational tools that allow for their implementation.

Published Since	2014
Journal Status	Active

Impact Factor	7.4

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- A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models
  
  Namjoon Suh, and Guang Cheng
- Models and Rating Systems for Head-to-Head Competition
  
  Mark E. Glickman, and Albyn C. Jones
- A Review of Reinforcement Learning in Financial Applications
  
  Yahui Bai, Yuhe Gao, Runzhe Wan, Sheng Zhang, and Rui Song
- Joint Modeling of Longitudinal and Survival Data
  
  Jane-Ling Wang, and Qixian Zhong
- Neural Methods for Amortized Inference
  
  Andrew Zammit-Mangion, Matthew Sainsbury-Dale, and Raphaël Huser
- Empirical Likelihood in Functional Data Analysis
  
  Hsin-wen Chang, and Ian W. McKeague
- Excess Mortality Estimation
  
  Jon Wakefield, and Victoria Knutson
- Infectious Disease Modeling
  
  Jing Huang, and Jeffrey S. Morris
- Tensors in High-Dimensional Data Analysis: Methodological Opportunities and Theoretical Challenges
  
  Arnab Auddy, Dong Xia, and Ming Yuan
- Designs for Vaccine Studies
  
  M. Elizabeth Halloran
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- Functional Data Analysis
  
  Jane-Ling Wang, Jeng-Min Chiou, and Hans-Georg Müller
  
  Vol. 3 (2016), pp. 257–295
- Probabilistic Forecasting
  
  Tilmann Gneiting, and Matthias Katzfuss
  
  Vol. 1 (2014), pp. 125–151
- Bayesian Computing with INLA: A Review
  
  Håvard Rue, Andrea Riebler, Sigrunn H. Sørbye, Janine B. Illian, Daniel P. Simpson, and Finn K. Lindgren
  
  Vol. 4 (2017), pp. 395–421
- Algorithmic Fairness: Choices, Assumptions, and Definitions
  
  Shira Mitchell, Eric Potash, Solon Barocas, Alexander D'Amour, and Kristian Lum
  
  Vol. 8 (2021), pp. 141–163
- Functional Regression
  
  Jeffrey S. Morris
  
  Vol. 2 (2015), pp. 321–359
- Topological Data Analysis
  
  Larry Wasserman
  
  Vol. 5 (2018), pp. 501–532
- Finite Mixture Models
  
  Geoffrey J. McLachlan, Sharon X. Lee, and Suren I. Rathnayake
  
  Vol. 6 (2019), pp. 355–378
- Q-Learning: Theory and Applications
  
  Jesse Clifton, and Eric Laber
  
  Vol. 7 (2020), pp. 279–301
- Learning Deep Generative Models
  
  Ruslan Salakhutdinov
  
  Vol. 2 (2015), pp. 361–385
- Microbiome, Metagenomics, and High-Dimensional Compositional Data Analysis
  
  Hongzhe Li
  
  Vol. 2 (2015), pp. 73–94
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Annual Review of Statistics and Its Application

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