Journal Statistics

171
Journal of the Royal Statistical Society Series B
papers tracked
400
Journal of the American Statistical Association
papers tracked
161
Annals of Statistics
papers tracked
113
Biometrika
papers tracked
320
Journal of Machine Learning Research
papers tracked

Recent Papers

AOS Jun 16, 2026
Nuisance Function Tuning and Sample Splitting for Optimally Estimating a Doubly Robust Functional

Rajarshi Mukherjee, Sean McGrath

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JRSSB Jun 16, 2026
Seconder of the vote of thanks to Farewell et al. and contribution to the Discussion of Regression by composition'

Peter McCullagh

Machine Learning
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JRSSB Jun 16, 2026
Safaa K Kadhem's contribution to the Discussion of ‘Regression by composition’ by Farewell et al

Safaa K Kadhem

Machine Learning
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JRSSB Jun 15, 2026
Cheng et al's contribution to the Discussion of ‘Regression by composition’ by Farewell et al

Jiangfeng Wang, Keming Yu, Rong Jiang et al.

Machine Learning
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JRSSB Jun 15, 2026
Gaffi, Legramanti, and Rimella’s contribution to the Discussion of ‘Regression by Composition’ by Farewell et al.

Francesco Gaffi, Lorenzo Rimella, Sirio Legramanti

Machine Learning
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JRSSB Jun 15, 2026
Archer Gong Zhang's contribution to the Discussion of ‘Regression by composition’ by Farewell et al

Archer Gong Zhang

Machine Learning
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JRSSB Jun 15, 2026
Ruixuan Zhao, Oliver Dukes, Linbo Wang, and Lin Liu’s contribution to the Discussion of ‘Regression by composition’ by Farewell et al

Lin Liu, Linbo Wang, Oliver Dukes et al.

Machine Learning
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JRSSB Jun 15, 2026
Jingxin Yan, Lin Liu, Oliver Dukes, Qizhai Li, and Linbo Wang’s contribution to the Discussion of ‘Regression by composition’ by Farewell et al.

Lin Liu, Linbo Wang, Oliver Dukes et al.

Machine Learning
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JRSSB Jun 15, 2026
Maozai Tian, Wei Xiong, Shaopei Ma, and Jinwen Liang's Contribution to the Discussion of ‘Regression by Composition’ by Farewell et al

Maozai Tian, Shaopei Ma, Wei Xiong et al.

Machine Learning
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JRSSB Jun 15, 2026
Mei Dong, Linbo Wang, Lin Liu, and Oliver Dukes's contribution to the Discussion of ‘Regression by composition’ by Farewell et al

Lin Liu, Linbo Wang, Oliver Dukes et al.

Machine Learning
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JRSSB Jun 15, 2026
Arun Chind’s contribution to the discussion of ‘Regression by composition’ by Farewell et al

Arun Peter Chind

Machine Learning
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JRSSB Jun 15, 2026
Andi Wang's contribution to the Discussion of ‘Regression by composition’ by Farewell et al

Andi Q Wang

Machine Learning
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JASA Jun 15, 2026
Enhanced localized conformal prediction with imperfect auxiliary information

Changliang Zou, Yinjie Min, Liuhua Peng

Statistical Learning
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JRSSB Jun 13, 2026
Andrej Srakar’s contribution to the Discussion of ‘Regression by composition' by Farewell et al

Andrej Srakar

Machine Learning
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JRSSB Jun 13, 2026
Si-Yang Li, David van Dyk and Maximilian Autenreith's contribution to the Discussion of ‘Regression by composition’ by Farewell et al

Si-Yang Li, David A van Dyk, Maximilian Autenrieth

Machine Learning
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JASA Jun 12, 2026
Online Change Rate Learning for Functional Data

Deru Kong, Ping Yu, Tiejun Tong et al.

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JASA Jun 12, 2026
Variational Nonparametric Inference in Stochastic Block Models with Functional Covariates

Peijun Sang, Zuofeng Shang, Yang Feng et al.

Nonparametric Statistics
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JASA Jun 12, 2026
Deconfounding via Profiled Transfer Learning

Fang Yao, Jingyuan Liu, Ziyuan Chen et al.

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Biometrika Jun 11, 2026
Doubly robust identification for bivariate causal discovery under unmeasured confounding

Rui Duan, Sai Li, Wei Li

Causal Inference
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JRSSB Jun 11, 2026
Sampling from high-dimensional, multimodal distributions using automatically tuned, tempered Hamiltonian Monte Carlo

Joonha Park

High-Dimensional Statistics Computational Statistics
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