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.

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Impact Factor	8.7

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- Deep Spatiotemporal Point Processes: Advances and New Directions
  
  Xiuyuan Cheng, Zheng Dong and Yao Xie
- The Why and How of Convex Clustering
  
  Eric C. Chi, Aaron J. Molstad, Zheming Gao and Jocelyn T. Chi
- Statistical Aspects of Racial and Ethnic Health Disparities
  
  Jay S. Kaufman
- Operator Learning: A Statistical Perspective
  
  Unique Subedi and Ambuj Tewari
- Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches
  
  Deependra K. Thapa, Erik S. Parker, Mounika Kandukuri, Xi (Rita) Wang, Thirupathi R. Mokalla, Olivia C. Robertson, Wasiuddin Najam, Andrew E. Teschendorff, Andrew W. Brown, John R. Speakman, Yisheng Peng, Bernard S. Gorman, Heping Zhang, Luis-Enrique Becerra-Garcia, Colby J. Vorland and David B. Allison
- Statistical Learning for Functional Data
  
  Jiguo Cao, Sidi Wu, Muye Nanshan, Haolun Shi and Liangliang Wang
- Proper Scoring Rules for Estimation and Forecast Evaluation
  
  Kartik Waghmare and Johanna Ziegel
- Optimal Designs for Correlated Data
  
  J. López-Fidalgo and W.K. Wong
- The Natural Value of Treatment and Its Importance for Causal Inference
  
  Aaron L. Sarvet and Mats J. Stensrud
- Statistics for Animal Tracking Data
  
  Vianey Leos-Barajas, Ignacio Alvarez-Castro and Juan M. Morales
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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
- Finite Mixture Models
  
  Geoffrey J. McLachlan, Sharon X. Lee and Suren I. Rathnayake
  
  Vol. 6 (2019), pp. 355–378
- Statistical Aspects of Wasserstein Distances
  
  Victor M. Panaretos and Yoav Zemel
  
  Vol. 6 (2019), pp. 405–431
- Algorithmic Fairness: Choices, Assumptions, and Definitions
  
  Shira Mitchell, Eric Potash, Solon Barocas, Alexander D'Amour and Kristian Lum
  
  Vol. 8 (2021), pp. 141–163
- Q-Learning: Theory and Applications
  
  Jesse Clifton and Eric Laber
  
  Vol. 7 (2020), pp. 279–301
- Granger Causality: A Review and Recent Advances
  
  Ali Shojaie and Emily B. Fox
  
  Vol. 9 (2022), pp. 289–319
- Topological Data Analysis
  
  Larry Wasserman
  
  Vol. 5 (2018), pp. 501–532
- Functional Regression
  
  Jeffrey S. Morris
  
  Vol. 2 (2015), pp. 321–359
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Annual Review of Statistics and Its Application

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