Found 145 papers
Sorted by: Newest FirstStratum order-of-addition designs
Ze Liu, Min-Qian Liu, Liushan Zhou et al.
Abstract Order-of-addition experiments are widely employed in many fields of science and industry to study how the order of componen...
Causal K-means clustering
Edward H Kennedy, Kwangho Kim, Jisu Kim
Abstract Causal effects are often characterized at the population level, which can mask important heterogeneity across latent subgro...
Post-detection inference for sequential changepoint localization
Aaditya Ramdas, Aytijhya Saha
Abstract This article addresses a fundamental but largely unexplored challenge in sequential changepoint analysis: conducting infere...
Gaussianized design optimization for covariate balance in randomized experiments
Tengyuan Liang, Wenxuan Guo, Panos Toulis
Abstract Achieving covariate balance in randomized experiments enhances the precision of treatment effect estimation. However, exist...
Authors’ reply to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smith et al
David Bruns-Smith, Oliver Dukes, Avi Feller et al.
Byzantine-tolerant distributed learning of finite mixture models
Yan Shuo Tan, Qiong Zhang, Jiahua Chen
Abstract Traditional statistical methods need to be updated to work with modern distributed data storage paradigms. The split-and-co...
Autoregressive networks with dependent edges
Qiwei Yao, Jinyuan Chang, Qin Fang et al.
Abstract We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses models that acco...
Generalized point process additive models
Bing Li, Kuang-Yao Lee, Jiehuan Sun et al.
Abstract In this article, we propose a generalized point process additive model with a scalar response and high-dimensional point pr...
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.
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
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
Thomas Maullin’s contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al
Thomas Maullin-Sapey
Joshua Agterberg’s contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al
Joshua Agterberg
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
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
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
Masaaki Imaizumi's contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al
Masaaki Imaizumi
Gesine Reinert’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al’
Gesine Reinert
Wanjie Wang's contribution to the Discussion of `Statistical exploration of the Manifold Hypothesis' by Whiteley et al
Wanjie Wang
Alexander Modell’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al
Alexander Modell
Safaa K. Kadhem's contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al
Safaa K Kadhem
Joshua Cape’s contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al
Joshua Cape
Authors reply to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al
Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
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...
Yanbo Tang’s contribution to the discussion of “Statistical exploration of the Manifold Hypothesis” by Whiteley et al
Yanbo Tang
Michael Trosset's contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whitely et al
Michael W Trosset
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
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
Melanie Weber’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al
Melanie Weber
Martin Schlather & Milan Stehlik’s contribution to the Discussion of “Statistical exploration of the Manifold Hypothesis” by Whiteley et al
Martin Schlather
Modelling with categorical features via exact fusion and sparsity regularization
Kayhan Behdin, Rahul Mazumder, Riade Benbaki et al.
Abstract We study the high-dimensional linear regression problem with categorical predictors that have many levels. We propose a new...
Combining evidence across filtrations
Aaditya Ramdas, Yo Joong Choe
Abstract In sequential anytime-valid inference, any admissible procedure must be based on e-processes: generalizations of test marti...
Nonparametric inference for censored data using deep neural networks
Guosheng Yin, Jian Huang, Xingqiu Zhao et al.
Abstract We propose a novel deep learning approach to nonparametric statistical inference for the conditional hazard function of sur...
Model privacy: a unified framework for understanding model stealing attacks and defences
Ganghua Wang, Yuhong Yang, Jie Ding
Abstract The use of machine learning (ML) has become increasingly prevalent in various domains, highlighting the importance of under...
Beyond the mean: limit theory and tests for infinite-mean autoregressive conditional durations
Giuseppe Cavaliere, Thomas Mikosch, Anders Rahbek et al.
Abstract Integrated autoregressive conditional duration (ACD) models serve as counterparts to integrated generalized autoregressive ...
Double cross-fit doubly robust estimators: Beyond series regression
Larry Wasserman, Sivaraman Balakrishnan, Alec McClean et al.
Abstract Double cross-fit doubly robust (DCDR) estimators, which train nuisance function estimators on separate samples, are effecti...
Proximal causal inference for conditional separable effects
Chan Park, Mats J Stensrud, Eric J Tchetgen Tchetgen
Abstract Scientists regularly pose questions about treatment effects on outcomes conditional on a posttreatment event. However, caus...
Statistical inference for cell type deconvolution
Lin Gui, Dongyue Xie, Jingshu Wang
Abstract Integrating heterogeneous datasets across different measurement platforms poses fundamental challenges for statistical infe...
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
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
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. ...
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
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
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...
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
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
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
Cheng and Tong’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al
Bing Cheng, Howell Tong
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
Ian Gallagher’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al
Ian Gallagher
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...
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
Safaa K. Kadhem's contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smith et al
Safaa K Kadhem
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.
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
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 “Statistical exploration of the Manifold Hypothesis” by Nick Whiteley et al”
Garib Nath Singh
Yinqiu He’s contribution to the Discussion of ’Statistical exploration of the Manifold Hypothesis’ by Whiteley et al
Yinqiu He
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
Dr Arun Chind’s contribution to the Discussion of Statistical exploration of the Manifold Hypothesis by Whiteley, et al
Arun Peter Chind
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
Root cause discovery via permutations and Cholesky decompositionGet access
Jinzhou Liand 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
Multilayer random dot product graphs: estimation and online change point detection
Fan Wangand 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
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
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
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