Found 94 papers
Sorted by: Newest FirstComparing causal parameters with many treatments and positivity violations
A Mcclean, Y Li, S Bae et al.
Summary Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a ...
Dynamic covariate balancing: estimating treatment effects over time with potential local projections
Jelena Bradic, Davide Viviano
Abstract This article concerns the estimation and inference of treatment effects in panel data settings when treatments change dynam...
Characterizing extremal dependence on a hyperplane
P Wan
Summary In this paper, we characterize the extremal dependence of d asymptotically dependent variables using a class of random vecto...
DiaconisâYlvisaker prior penalized likelihood for đ/đ â đż â (0,1) logistic regression
P Sterzinger, I Kosmidis
Summary We characterize the behaviour of the maximum DiaconisâYlvisaker prior penalized likelihood estimator in high-dimensional log...
Asymptotics for a class of parametric martingale posteriors
E Fong, A Yiu
Summary The martingale posterior framework replaces the elicitation of the likelihood and prior with that of a sequence of one-step-...
Inferring manifolds using Gaussian processes
David B Dunson, Nan Wu
It is often of interest to infer lower-dimensional structure underlying complex data. As a flexible class of nonlinear structures, it is common to foc...
Design-based Causal Inference for Incomplete Block Designs
Taehyeon Koo, Nicole E Pashley
Abstract Researchers often turn to block randomization to increase the precision of their inference or due to practical consideratio...
Parameterising the effect of a continuous treatment using average derivative effects
Stijn Vansteelandt, Oliver J Hines, Karla Diaz-Ordaz
Abstract The average treatment effect (ATE) is commonly used to quantify the main effect of a binary treatment on an outcome. Extens...
Randomization-Based Confidence Sets for the Local Average Treatment Effect
P M Aronow, Haoge Chang, Patrick Lopatto
Summary We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinemen...
Structural restrictions in local causal discovery: identifying direct causes of a target variable
J Bodik, V Chavez-Demoulin
Abstract
Harnessing The Collective Wisdom: Fusion Learning Using Decision Sequences from Diverse SourcesGet access
T Banerjeeand others
A family of toroidal diffusions with exact likelihood inferenceGet access
E GarcĂa-PortuguĂŠsandM Sørensen
Model-free selective inference under covariate shift via weighted conformal p-valuesGet access
Ying JinandEmmanuel J Candès
Uniform inference in linear mixed modelsGet access
Karl Oskar EkvallandMatteo Bottai
Planning for gold: Hypothesis screening with split samples for valid powerful testing in matched observational studiesGet access
William Bekermanand others
Extremal correlation coefficient for functional dataGet access
M KimandP Kokoszka
Spatial self-confounding: Smoothness-related estimation bias in spatial regression models
David BolinandJonas Wallin
Identification and estimation of interaction effects in nonparametric additive regressionGet access
Seung Hyun Moonand others
Regression graphs and sparsity-inducing reparametrizations
J Rybakand others
High-dimensional covariance estimation by pairwise likelihood truncation
A Casa, D Ferrari, Z Huang
Abstract Pairwise likelihood is an approximation of the full likelihood function that facilitates the analysis of high-dimensional c...
Bounds on causal effects in 2đ˛ factorial experiments with non-compliance
M Blackwell, N E Pashley
Summary Factorial experiments are ubiquitous in the social and biomedical sciences, but when units fail to comply with each assigned...
Estimating Ratios of Means of Multicategory Data Observed with Sample and Category Perturbations
D S Clausen, S V Teichman, A D Willis
Summay We consider the problem of estimating ratios of means of a multivariate outcome across covariates when the data are observed ...
Higher criticism for rare and weak non-proportional hazard deviations in survival analysis
A Kipnisand others
Post-selection inference for causal effects after causal discoveryGet access
T Changand others
Decomposing Gaussians with Unknown CovarianceGet access
A Dharamshiand others
Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical TrialsGet access
B Renand others
Geodesic Optimal Transport Regression
Hans-Georg MĂźller, Changbo Zhu
Abstract Classical regression models do not cover non-Euclidean data that reside in a general metric space, while the current litera...
A frequentist local false discovery rate
William Fithian, Daniel Xiang, Jake A Soloff
Abstract The local false discovery rate (lfdr) of Efron et al. (2001) enjoys major conceptual and decision-theoretic advantages over...
Geodesic slice sampling on Riemannian manifolds
Alain Durmus, Samuel Gruffaz, Mareike Hasenpflug et al.
Summary We propose a theoretically justified and practically applicable slice-sampling-based Markov chain Monte Carlo method for app...
Treatment Choice with Nonlinear Regret
Toru Kitagawa, Sokbae Lee, Chen Qiu
Abstract Following Savage (1951) and Manski (2004), the literature of statistical treatment choice focuses on the mean of welfare re...
On the consistency of bootstrap for matching estimators
Ziming Lin, Fang Han
Abstract In a landmark paper, abadie2008failure showed that the naive bootstrap is inconsistent when applied to nearest neighbour ma...
Assumption-Lean Post-Integrated Inference with Surrogate-Control Outcomes
Larry Wasserman, Jin-Hong Du, Kathryn Roeder
Summary Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variati...
Testing for latent structure via the Wilcoxon--Wigner random matrix of normalized rank statistics
Joshua Cape, Jonquil Z Liao
Summary This paper considers the problem of testing for latent structure in large symmetric data matrices. The goal here is to devel...
Generalized FrĂŠchet means with random minimizing domains and its strong consistency
Jaesung Park, Sungkyu Jung
Abstract This paper introduces a novel extension of FrĂŠchet means, referred to as generalized FrĂŠchet means, as a comprehensive fram...
Calibrated sensitivity models
A Mcclean, Z Branson, E H Kennedy
Abstract In causal inference, sensitivity models are used to assess how unmeasured confounders could alter causal analyses, but the ...
Estimating the number of significant components in high-dimensional PCA
Bo Zhang, Guangming Pan, ZhiXiang Zhang
SUMMARY We consider the problem of estimating the number of significant components in high-dimensional principal component analysis ...
Tail-robust factor modelling of vector and tensor time series in high dimensions
Haeran Cho, Matteo Barigozzi, Hyeyoung Maeng
Summary We study the problem of factor modelling vector- and tensor-valued time series in the presence of heavy tails in the data, w...
Spectral estimation for point processes and random fields
J P Grainger, T A Rajala, D J Murrell et al.
Summary Spatial variables can be observed in many different forms, such as regularly sampled random fields (lattice data), point pro...
Characteristic function-based tests for spatial randomness
Yiran Zeng, Dale L Zimmerman
Abstract We introduce a new type of test for complete spatial randomness that applies to mapped point patterns in a rectangle or a c...
A spectral method for multi-view subspace learning using the product of projections
R Sergazinov, A Taeb, I Gaynanova
Summary Multi-view data provides complementary information on the same set of observations, with multi-omics and multimodal sensor d...
Asymptotic Validity and Finite-Sample Properties of Approximate Randomization Tests
P Toulis
Abstract Randomization tests rely on simple data transformations and possess an appealing robustness property. In addition to being ...
Sparse higher order partial least squares for simultaneous variable selection, dimension reduction and tensor denoising
Kwangmoon Park, SĂźndĂźz KeleĹ
Abstract Motivated by the challenge of estimating effects of DNA methylation on 3D genomic contacts captured by multi-modal single c...
A more robust approach to multivariable Mendelian randomization
Yinxiang Wu, Hyunseung Kang, Ting Ye
Summary Multivariable Mendelian randomization uses genetic variants as instrumental variables to infer the direct effects of multipl...
Goodness-of-fit tests for linear non-Gaussian structural equation models
D Schkoda, M Drton
Abstract
Fast convergence of the Expectation-Maximization algorithm under a logarithmic Sobolev inequality
R CaprioandA M Johansen
On testing Kronecker product structure in tensor factor models
Z CenandC Lam
Sensitivity Analysis for Observational Studies with Flexible Matched DesignsGet access
Xinran Li
Priors for second-order unbiased Bayes estimatorsGet access
Mana Sakaiand others
Alternative Mean Square Error Estimators and Confidence Intervals for Small Area Prediction Under General DesignsGet access
Yanghyeon ChoandEmily Berg
Supervised Contamination Detection, with Flow Cytometry ApplicationGet access
S Gaucherand others
Resampling Methods with Multiply Imputed Data
Michael W Robbins, Lane Burgette
Abstract
Infinite joint species distribution models
D B Dunson, F Stolf
Abstract
Existence and Applications of Finite Population Samples that are Exactly Balanced
Yves TillĂŠ, Louis-Paul Rivest
Abstract
Factor pre-training in Bayesian multivariate logistic models
D B Dunson, L Mauri
Abstract
Factor pre-training in Bayesian multivariate logistic modelsGet access
L MauriandD B Dunson
Dimension estimation in a spiked covariance model using high-dimensional data augmentation
U RadojiÄiÄ, J Virta
Abstract
A More Robust Approach to Multivariable Mendelian Randomization
Yinxiang Wu, others
Abstract
Decomposing Gaussians with Unknown Covariance
A Dharamshi, others
Abstract
A general condition for bias attenuation by a nondifferentially mismeasured confounder
Jeffrey Zhang, Junu Lee
Summary In real-world studies, the collected confounders may suffer from measurement error. Although mismeasurement of confounders is t...
Debiased learning of the causal net benefit with censored event time data
Torben Martinussen, Stijn Vansteelandt
Abstract
Simulating diffusion bridges with score matching
J Heng, others
Abstract
Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials
B Ren, others
Abstract
Identifying and bounding the probability of necessity for causes of effects with ordinal outcomes
Chao Zhang, others
Abstract
A family of toroidal diffusions with exact likelihood inference
E GarcĂa-PortuguĂŠs, M Sørensen
Abstract
Pseudo-likelihood Estimators for Graphical Models: Existence and Uniqueness
B Roycraft, B Rajaratnam
Abstract
A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning
Pan Zhao, Yifan Cui
Abstract
Multicalibration for Modeling Censored Survival Data with Universal Adaptability
Hanxuan Ye, Hongzhe Li
Abstract
Multicalibration for Modeling Censored Survival Data with Universal AdaptabilityGet access
Hanxuan YeandHongzhe Li
Nonsense associations in Markov random fields with pairwise dependence
Sohom Bhattacharya, others
Abstract
Nonsense associations in Markov random fields with pairwise dependenceGet access
Sohom Bhattacharyaand others
Aggregating Dependent Signals with Heavy-Tailed Combination Tests
Lin Gui, others
Abstract
Robust functional principal component analysis for non-Euclidean random objects
Jiazhen Xu, others
Abstract
Consistent and Scalable Composite Likelihood Estimation of Probit Models with Crossed Random Effects
R Bellio, others
Abstract
Powerful Partial Conjunction Hypothesis Testing via Conditioning
B Liang, others
Abstract
Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
Tong Xu, others
Abstract
Bias correction of quadratic spectral estimators
Lachlan C Astfalck, others
Abstract
Towards a turnkey approach for unbiased Monte Carlo estimation of smooth functions of expectations
Nicolas Chopin, others
Abstract
A spike-and-slab prior for dimension selection in generalized linear network eigenmodels
Joshua D Loyal, Yuguo Chen
Abstract
An omitted variable bias framework for sensitivity analysis of instrumental variables
Carlos Cinelli, Chad Hazlett
Abstract We develop an omitted variable bias framework for sensitivity analysis of instrumental variable estimates that naturally handl...
Nonparametric data segmentation in multivariate time series via joint characteristic functions
E T McGonigle, H Cho
Summary Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in ...
Testable implications of outcome-independent missingness not at random in covariates
A SjÜlander, S Hägg
Summary A common aim of empirical research is to regress an outcome on a set of covariates, when some covariates are subject to missing...
Continuous-time locally stationary wavelet processes
H A Palasciano, M I Knight, G P Nason
Abstract This article introduces the class of continuous-time locally stationary wavelet processes. Continuous-time models enable us to...
On the fundamental limitations of multi-proposal Markov chain Monte Carlo algorithms
F Pozza, G Zanella
Summary We study multi-proposal Markov chain Monte Carlo algorithms, such as multiple-try or generalized MetropolisâHastings schemes, w...
Randomization inference when N equals one
Tengyuan Liang, Benjamin Recht
Summary For decades, $ N $-of-1 experiments, where a unit serves as its own control and treatment in different time windows, have been ...
Noise-induced randomization in regression discontinuity designs
Dean Eckles, Nikolaos Ignatiadis, Stefan Wager et al.
Summary Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running...
High-dimensional Factor Analysis for Network-linked data
Jinming Li, others
Abstract
âOn the behaviour of marginal and conditional AIC in linear mixed modelsâ
Sonja Greven, Thomas Kneib