Statistical exploration of the Manifold Hypothesis

Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy

Abstract The Manifold Hypothesis is a widely accepted tenet of Machine Learning which asserts that nominally high-dimensional data a...

The causal effects of modified treatment policies under network interference

Salvador V Balkus, Scott W Delaney, Nima S Hejazi

Abstract Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of contin...

Anytime validity is free: inducing sequential tests

Nick W Koning, Sam van Meer

Abstract Anytime valid sequential tests permit us to stop testing based on the current data, without invalidating the inference. Giv...

The synthetic instrument: from sparse association to sparse causation

Dehan Kong, Dingke Tang, Linbo Wang

Abstract In many observational studies, researchers are often interested in the effects of multiple exposures on a single outcome. S...

Pitman efficiency lower bounds for multivariate distribution-free tests based on optimal transport

Nabarun Deb, Bhaswar B Bhattacharya, Bodhisattva Sen

Abstract The Wilcoxon rank sum test is one of the most popular distribution-free two-sample tests for univariate data. Among the imp...

Liyang Sun's contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al

Liyang Sun

Inference on function-valued parameters using a restricted score test

Marco Carone, Ali Shojaie, Aaron Hudson

Abstract It is often of interest to make inference on an unknown function that is a local parameter of the data-generating mechanism...

Online kernel CUSUM for change-point detection

Song Wei, Yao Xie

Abstract We present a computationally efficient online kernel Cumulative Sum method for change-point detection that utilizes the max...

Penalized empirical likelihood over decentralized networks

Jinye Du, Qihua Wang

Abstract Empirical likelihood encounters serious computational challenges when applied to massive datasets or multiple data sources ...

Doss and Huling's contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smith et al

Charles R Doss, Jared D Huling

Martin Schlather & Milan Stehlik’s contribution to the Discussion of “Statistical exploration of the Manifold Hypothesis” by Whiteley et al

Martin Schlather

ART: distribution-free and model-agnostic changepoint detection with finite-sample guarantees

Guanghui Wang, Changliang Zou, Xiaolong Cui et al.

Abstract We introduce ART, a distribution-free and model-agnostic framework for changepoint analysis with finite-sample guarantees. ...

Zhu Shen and José R. Zubizarreta’s contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smith et al

Zhu Shen, José R Zubizarreta

Tian, Liu and Tan's contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al

Maozai Tian, Shuo Liu, Tan Meng

Safaa K. Kadhem's contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Safaa K Kadhem

Alexander Modell’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Alexander Modell

Wanjie Wang's contribution to the Discussion of `Statistical exploration of the Manifold Hypothesis' by Whiteley et al

Wanjie Wang

Gesine Reinert’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al’

Gesine Reinert

Masaaki Imaizumi's contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Masaaki Imaizumi

Rocco Caprio and Adam Johansen's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al

Rocco Caprio, Adam M Johansen

Junhyung Chang and Xiaoyu Lei's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al

Junhyung Chang, Xiaoyu Lei

Zihao Wen and David L. Dowe's contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Zihao Wen, David L Dowe

Seconder of the vote of thanks to Whiteley et al. and Contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’

Eddie Aamari

Joshua Agterberg’s contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al

Joshua Agterberg

Rotnitzky, Smucler and Robins contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al’

Andrea Rotnitzky, Ezequiel Smucler, James M Robins

Proposer of the vote of thanks to Whiteley et al. and contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’

Tengyao Wang

Thomas Maullin’s contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al

Thomas Maullin-Sapey

Joshua Cape’s contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al

Joshua Cape

Poorbita Kundu’s and Johannes Schmidt-Hieber’s contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al

Johannes Schmidt-Hieber, Poorbita Kundu

Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models

Giacomo Zanella, Filippo Ascolani, Gareth O Roberts

Abstract We study general coordinate-wise Markov chain Monte Carlo schemes (such as Metropolis-within-Gibbs samplers), which are com...

Skew-symmetric approximations of posterior distributions

Daniele Durante, Botond Szabo, Francesco Pozza

Abstract Popular deterministic approximations of posterior distributions from, e.g. the Laplace method, variational Bayes and expect...

Jiangfeng Wang, Keming Yu and Rong Jiang's contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al

Jiangfeng Wang, Keming Yu, Rong Jiang

Melanie Weber’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Melanie Weber

Shan, Ying and Zhao’s contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smith et al

Jiwei Zhao, Jiawei Shan, Chao Ying

Simon et al. Contribution to the Discussion of “Statistical exploration of the Manifold Hypothesis” by Whiteley et al

Emilio Porcu, Horst Simon, Mohammed El-Amine Azz et al.

Proposer of the vote of thanks to Bruns-Smith et al. and contribution to the Discussion of ‘Augmented balancing weights as linear regression'

Lin Liu

Fukang Zhu and Xiangyu Guo’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Fukang Zhu, Xiangyu Guo

Sanna Passino and Heard’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Francesco Sanna Passino, Nicholas A Heard

Michael Trosset's contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whitely et al

Michael W Trosset

Yanbo Tang’s contribution to the discussion of “Statistical exploration of the Manifold Hypothesis” by Whiteley et al

Yanbo Tang

Kiho Park, Yo Joong Choe, and Yibo Jiang's contribution to the Discussion of `Statistical exploration of the Manifold Hypothesis' by Whitely, Gray and Rubin-Delanchy

Kiho Park, Yo Joong Choe, Yibo Jiang

Cheng and Tong’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Bing Cheng, Howell Tong

Safaa K. Kadhem's contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smith et al

Safaa K Kadhem

Alberto Bordino and Olga Klopp’s contribution to the Discussion of “Statistical exploration of the Manifold Hypothesis” by Whiteley et al

Alberto Bordino, Olga Klopp

Multiple randomization designs: estimation and inference with interference

Lorenzo Masoero, Suhas Vijaykumar, Thomas S Richardson et al.

Abstract Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practic...

Ian Gallagher’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Ian Gallagher

M. Stehlík and M. Schlather's contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Martin Schlather, Milan Stehlík

Andrew Gelman’s contribution to the discussion of “Statistical exploration of the manifold hypothesis” by Whiteley et al

Andrew Gelman

Tian, Ma, Yu and Hu’s Contribution to the Discussion of ‘Statistical Exploration of the Manifold Hypothesis’ by Whiteley et al

Maozai Tian, Shaopei Ma, Zhen Yu et al.

Dr Arun Chind’s contribution to the Discussion of Statistical exploration of the Manifold Hypothesis by Whiteley, et al

Arun Peter Chind

Seconder of the vote of thanks to Bruns-Smith et al. and contribution to the Discussion of ‘Augmented balancing weights as linear regression'

Andrej Srakar

Supriya Tiwari and Pallavi Basu's contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al

Supriya Tiwari, Pallavi Basu

Yinqiu He’s contribution to the Discussion of ’Statistical exploration of the Manifold Hypothesis’ by Whiteley et al

Yinqiu He

“Professor Garib Nath Singh’s contribution to the Discussion of “Statistical exploration of the Manifold Hypothesis” by Nick Whiteley et al”

Garib Nath Singh

N.T. Longford's contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smith et al

Nicholas T Longford

Professor Garib Nath Singh’s contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smith et al

Garib Nath Singh

A new integrative learning framework for integrating multiple secondary outcomes into primary outcome analysis: a case study on liver health

Shuo Chen, Chixiang Chen, Daxuan Deng et al.

Abstract In the era of big data, secondary outcomes have become increasingly important alongside primary outcomes. These secondary o...

Federated feature selection with false discovery rate control

Runze Li, Jiayi Tong, Jie Hu et al.

Abstract Selecting a set of universally relevant features associated with a given response variable across multiple distributed data...

Using a two-parameter sensitivity analysis framework to efficiently combine randomized and nonrandomized studies

Bikram Karmakar, Ruoqi Yu, Jessica Vandeleest et al.

Abstract Causal inference is vital for informed decision-making across fields such as biomedical research and social sciences. Rando...

Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraintsGet access

Arnab Auddyand others

Self-organizing state-space models with artificial dynamics

Yuan Chenand others

Coloured Gaussian directed acyclic graphical models

Tobias Boegeand others

Correction to: Inference of dependency knowledge graph for Electronic Health Records

Root cause discovery via permutations and Cholesky decompositionGet access

Jinzhou Liand others

Generalized universal inference on risk minimizers

Neil Deyand others

Additive-Effect Assisted LearningGet access

Jiawei Zhangand others

Representation of context-specific causal models with observational and interventional data

Eliana DuarteandLiam Solus

Spectral change point estimation for high-dimensional time series by sparse tensor decompositionGet access

Xinyu ZhangandKung-Sik Chan

Bayesian analysis of product feature allocation models

Lorenzo Ghilottiand others

Inference of dependency knowledge graph for Electronic Health RecordsGet access

Zhiwei Xuand others

Robust detection of watermarks for large language models under human editsGet access

Xiang Liand others

Bootstrapping estimators based on the block maxima methodGet access

Axel BücherandTorben Staud

Scalable Bayesian inference for heat kernel Gaussian processes on manifoldsGet access

Junhui Heand others

A stratifiedL2-discrepancy with application to space-filling designsGet access

Ye TianandHongquan Xu

Simplifying debiased inference via automatic differentiation and probabilistic programmingGet access

Alex Luedtke

Huber means on Riemannian manifolds

Jongmin LeeandSungkyu Jung

Wasserstein generative regression

Shanshan Songand others

Multilayer random dot product graphs: estimation and online change point detection

Fan Wangand others

Statistical ranking with dynamic covariatesGet access

Pinjun Dongand others

Pretraining and the lassoGet access

Erin Craigand others

Censored quantile regression with time-dependent covariates

Chi Wing Chuand others

Online multivariate changepoint detection: leveraging links with computational geometryGet access

Liudmila Pishchaginaand others

Principal stratification with continuous post-treatment variables: nonparametric identification and semiparametric estimation

Sizhu Lu, others

Doubly robust conditional independence testing with generative neural networks

Yi Zhang, others

Estimating maximal symmetries of regression functions via subgroup lattices

Louis G Christie, John A D Aston

Harmonized estimation of subgroup-specific treatment effects in randomized trials: the use of external control data

Daniel Schwartz, others

Empirical likelihood for manifolds

Daisuke Kurisu, Taisuke Otsu

Efficient nonparametric estimators of discrimination measures with censored survival data

Torben Martinussen, Marie Skov Breum

Correction to: Parameterizing and simulating from causal models

Optimal clustering by Lloyd’s algorithm for low-rank mixture model

Dong Xia, Zhongyuan Lyu

A unified generalization of the inverse regression methods via column selection

Yin Jin, Wei Luo

Identification and multiply robust estimation in causal mediation analysis across principal strata

Chao Cheng, Fan Li

Ordinary differential equation models for a collection of discretized functions

Fang Yao, Lingxuan Shao

Correction to: Convexity and measures of statistical association

Least squares for cardinal paired comparisons data

Rahul Singh, others

Semiparametric localized principal stratification analysis with continuous strata

Yichi Zhang, Shu Yang

Orthogonalized moment aberration for mixed-level multi-stratum factorial designs with partially-relaxed orthogonal block structures

Ming-Chung Chang

Regularized halfspace depth for functional data

Hyemin Yeon, others

Goodness-of-fit tests for high-dimensional Gaussian graphical models via exchangeable sampling

Xiaotong Lin, others

Detection and inference of changes in high-dimensional linear regression with nonsparse structures

Haeran Cho, others

Isotonic mechanism for exponential family estimation in machine learning peer review

Yuling Yan, others

Covariate-assisted bounds on causal effects with instrumental variables

Alexander W Levis, others

Improving the false coverage rate adjusted confidence intervals

Tzviel Frostig, Yoav Benjamini

Bayesian mixture models with repulsive and attractive atoms

Mario Beraha, others

An optimal design framework for lasso sign recovery

Jonathan W Stallrich, others

A statistical view of column subset selection

Anav Sood, Trevor Hastie

Unbiased and consistent nested sampling via sequential Monte Carlo

Robert Salomone, others

SymmPI: predictive inference for data with group symmetries

Edgar Dobriban, Mengxin Yu

Product centred Dirichlet processes for Bayesian multiview clustering

Alexander Dombowsky, David B Dunson

Augmented balancing weights as linear regression

David Bruns-Smith, others

Graphical methods for Order-of-Addition experiments

Nicholas Rios, Dennis K J Lin

Confidence on the focal: conformal prediction with selection-conditional coverage

Ying Jin, Zhimei Ren

Convexity and measures of statistical association

Emanuele Borgonovo, others

Multi-resolution subsampling for linear classification with massive data

Haolin Chen, others

Sequential Monte Carlo testing by betting

Lasse Fischer, Aaditya Ramdas

Correction to: Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods

A general framework for cutting feedback within modularized Bayesian inference

Yang Liu, Robert J B Goudie

Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models

Tate Jacobson

Selecting informative conformal prediction sets with false coverage rate control

Ulysse Gazin, others

Conformal prediction with conditional guarantees

Isaac Gibbs, others

Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling

Jinyuan Chang, others

A conditioning tactic that increases design sensitivity in observational block designs

Paul R Rosenbaum

Adaptive experiments toward learning treatment effect heterogeneity

Waverly Wei, others

Semiparametric posterior corrections

Andrew Yiu, others

Two-phase rejective sampling and its asymptotic properties

Peng Ding, Shu Yang

Augmentation invariant manifold learning

Shulei Wang

Randomized empirical likelihood test for ultra-high dimensional means under general covariances

Yuexin Chen, others

Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation

Linda S L Tan