Found 144 papers
Sorted by: Newest FirstSemiparametrically efficient semi-supervised statistical inference
A Shojaie, Z Xu, D Witten
Summary We consider statistical inference under a semi-supervised setting where we have access to both labelled and unlabelled obser...
Test of partial effects for Fréchet regression on Bures-Wasserstein manifolds
Hongzhe Li, Haoshu Xu
Abstract We propose a novel test for assessing partial effects in Fréchet regression on the Bures-Wasserstein manifold. Under the nu...
Using fractional derivatives to derive marginal densities
S Y Li, D A van Dyk, M Autenrieth
Abstract This paper presents a novel method for analytical derivations of marginal densities using the fractional derivatives of mom...
General Frameworks for Conditional Two-Sample Testing
Ilmun Kim, Seongchan Lee, Suman Cha
Abstract We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distr...
Blockwise dynamic selection of smoothing parameters for kernel estimators of nonstationary densities of streaming data
F Camirand Lemyre, A Delaigle, J Mao
Abstract We consider nonparametric estimation of the nonstationary density of streaming data collected continuously over time. Those...
Testing matrix factor structure via generalized likelihood ratio
Tong Zhang, Xinbing Kong, Liwen Zhang
Abstract In this paper, we introduce a generalized likelihood ratio test for assessing whether the factor loading matrix in high-dim...
Saddlepoint approximations for plug-in resampling
Z Huang, Z Niu, J Ray Choudhury et al.
Abstract Resampling-based procedures can improve on normal approximations in sparse, large-scale testing problems, but their computa...
Rao-Blackwellized e-variables
D De Roos, B Chugg, P Grünwald et al.
Summary We show that for any concave utility, the expected utility of an e-variable can only increase after conditioning on a suffic...
Benign Overfitting Beyond Prediction: The Ordinary Least Squares Interpolator
Peng Ding, Dennis Shen, Dogyoon Song et al.
Summary Recent advances in deep learning have highlighted the phenomenon of benign overfitting in overparameterized statistical mode...
Optimal Watermark Generation under Type I and Type II Errors
Guang Cheng, Shirong Xu, Hengzhi He et al.
Abstract Watermarking has recently emerged as a crucial tool for protecting the intellectual property of generative models and for d...
Post-reduction inference for confidence sets of models
H S Battey, D G Rasines, Y Tang
Summary Sparsity in a regression context makes the model itself an object of interest, pointing to a confidence set of models as the...
Optimal and computationally tractable lower bounds for logistic log-likelihoods
Daniele Durante, Giacomo Zanella, Tommaso Rigon et al.
Summary The logit transform is arguably the most popular link function beyond linear settings. Its routine use, combined with the la...
Confidence Intervals for Linear Models with Arbitrary Noise Contamination
Dong Xie, Chao Gao, John Lafferty
Summary We study confidence interval construction for linear regression under Huber’s contamination model, where an unknown fraction...
Regularised Spectral Estimation for High-Dimensional Point Processes
C J Pinkney, C Euán, A J Gibberd et al.
Summary Advances in modern technology have enabled the simultaneous recording of neural spiking activity across large numbers of neu...
An optimal two-step estimation approach for two-phase studies
Q Zhou, K Y Wong
Summary Two-phase sampling is commonly adopted for reducing cost and improving estimation efficiency. In many two-phase studies, the...
Beyond Fixed Restriction Time: Adaptive Restricted Mean Survival Time Methods in Clinical Trials
Eric J Tchetgen Tchetgen, Jinghao Sun, Douglas E Schaubel
Summary Restricted mean survival time offers a compelling nonparametric alternative to hazard ratios for right-censored time-to-even...
The Multiplicative Instrumental Variable Model
Mengxin Yu, James M Robins, Chan Park et al.
Summary The instrumental variable (IV) design is a common approach to address hidden confounding bias. For validity, an IV must impa...
Geodesic difference-in-differences
Daisuke Kurisu, Taisuke Otsu, Yidong Zhou et al.
Summary Difference-in-differences (DID) is a widely used quasi-experimental design for causal inference, traditionally applied to sc...
Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI
Tijana Zrnic, Lihua Lei, Wenlong Ji
Abstract We establish a formal connection between the decades-old surrogate outcome model in biostatistics and economics and the eme...
Bias correction for Chatterjee’s graph-based correlation coefficient
Fang Han, Mona Azadkia, Leihao Chen
Summary Azadkia & Chatterjee (2021) recently introduced a simple nearest-neighbour graph-based correlation coefficient that cons...
Tight differencing in spectral density estimation with centrosymmetric kernels
Y Wang, K W Chan
Summary Mean-robust estimation of spectral density and long-run variance is crucial for many statistical inference procedures. Howev...
Revisiting Madigan and Mosurski: Collapsibility via Minimal Separators
Yi Sun, Jianhua Guo, Pei Heng et al.
Abstract Collapsibility provides a principled approach to dimension reduction in contingency tables and graphical models. Madigan &a...
Integral Probability Metric-Guided CUSUM-Net for Nonparametric Changepoint Detection
Guanghui Wang, Yunchen Li, Zhou Yu et al.
Abstract We propose CUSUM-Net, a nonparametric method for changepoint detection based on integral probability metrics and deep neura...
Identify the source of spikes: factor or mixture?
Guangming Pan, Zeqin Lin, Yiming Liu et al.
Summary We consider the problem of identifying the pattern of latent variables in high-dimensional linear latent variable models, wh...
Locally differentially private two-sample testing
A Kent, T B Berrett, Y Yu
Summary We consider the problem of two-sample testing under a local differential privacy constraint where a permutation procedure is...
Nonparametric Inference for Balance in Signed Networks
Weijing Tang, Xuyang Chen, Yinjie Wang
SUMMARY In many real-world networks, relationships often go beyond simple dyadic presence or absence; they can be positive, like fri...
Nonparametric estimators over metric graphs
Aldo Clemente, Eleonora Arnone, Jorge Mateu et al.
Abstract This work discusses a theory of functional spaces over metric graphs that permits the definition of penalized likelihood me...
Inference on covariance structure in high-dimensional multi-view data
D B Dunson, L Mauri
Summary This article focuses on covariance estimation for multi-view data. Popular approaches rely on factor-analytic decompositions...
On the inverse of covariance matrices for unbalanced crossed designs
Ziyang Lyu, S A Sisson, A H Welsh
Summary This paper addresses a long-standing open problem in crossed random effect models under unbalanced designs: how to find an a...
A new class of functional conditional autoregressive models
S Kim
Summary We introduce a new class of conditional autoregressive models for spatially dependent functional data, formulated through co...
Conditioning on posterior samples for flexible frequentist goodness-of-fit testing
Rina Foygel Barber, Ritwik Bhaduri, Aabesh Bhattacharyya et al.
Summary Tests of goodness of fit are used in nearly every domain where statistics is applied.One powerful and flexible approach is t...
Doubly Robust and Efficient Calibration of Prediction Sets for Right-Censored Time-to-Event Outcomes
Arun Kumar Kuchibhotla, Eric Tchetgen Tchetgen, Rebecca Farina
Summary Our objective is to construct well-calibrated prediction sets for a time-to-event outcome subject to right-censoring with gu...
Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
S Favaro, S Fortini
Summary The Poisson compound decision problem is a long-standing problem is statistics, for which empirical Bayes methods are common...
Convergence and Optimality of the EM Algorithm Under Multi-Component Gaussian Mixture Models
Xin Bing, Dehan Kong, Bingqing Li
Summary Gaussian mixture models are fundamental statistical tools for modeling heterogeneous data. Due to the nonconcavity of the li...
Aggregating conformal prediction sets via 𝜶-allocation
Changliang Zou, Zhaojun Wang, Congbin Xu et al.
Abstract Conformal prediction offers a distribution-free framework for constructing prediction sets with finite-sample coverage. Yet...
Automatic debiased machine learning for covariate shifts
V Chernozhukov, M Newey, W K Newey et al.
SUMMARY We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distribut...
Log-Gaussian Cox process on general metric graphs
David Bolin, Alexandre B Simas, Damilya Saduakhas
Summary The modelling of spatial point processes has advanced considerably, yet extending these models to non-Euclidean domains, suc...
Doubly robust identification for bivariate causal discovery under unmeasured confounding
Rui Duan, Sai Li, Wei Li
Summary Learning causal relationships between pairs of complex traits from observational studies is of great interest in many scient...
Robustness and Efficiency of Rosenbaum’s Rank-based Estimator in Randomized Trials: A Design-based Perspective
Bikram Karmakar, Nabarun Deb, Bodhisattva Sen et al.
Summary Mean-based estimators of causal effects in randomized experiments may behave poorly if the potential outcomes have a heavy t...
A Ball Divergence-Based Measure for Conditional Independence Testing With a Local Wild Bootstrap
Bhaswar B Bhattacharya, Bilol Banerjee, Anil K Ghosh
Abstract In this paper we introduce a new measure of conditional dependence between two random vectors X and Y given another random ...
Design Stability in Adaptive Experiments: Implications for Treatment Effect Estimation
Koulik Khamaru, Saikat Sengupta, Suvrojit Ghosh et al.
Abstract We study the problem of estimating the average treatment effect under sequentially adaptive treatment assignment mechanisms...
Asymmetric Penalties Underlie Proper Loss Functions in Probabilistic Forecasting
E Buchweitz, J V Romano, R J Tibshirani
Summary Accurately forecasting the probability distribution of phenomena of interest is a classic and ever more widespread goal in s...
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...
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 ...
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...
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...
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...
Parallel computations for Metropolis Markov chains with Picard maps
G Zanella, S Grazzi
Abstract We develop parallel algorithms for simulating zeroth-order (also known as gradient-free) Metropolis Markov chains based on ...
Finding Distributions that Differ, with False Discovery Rate Control
Edgar Dobriban, Eric Tchetgen Tchetgen, Yonghoon Lee
Summary We consider the problem of comparing a reference distribution with several other distributions. Given a sample from both the...
Non-parametric efficient estimation of marginal structural models with continuous time-varying treatments
A Martin, M Santacatterina, I Díaz
Summary Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In ...
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 García-PortuguésandM 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
Functional Principal Component Analysis for Sparse Censored Data
Caitrin Murphy, Eric Laber, Rhonda Merwin et al.
Summary Functional principal component analysis is a key tool in the study of functional data, driving both exploratory analyses and...
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...
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...
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 2𝑲 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...
An average-case sensitivity analysis for unmeasured confounding
Qingyuan Zhao, Yao Zhang
Summary Sensitivity analysis for the unconfoundedness assumption is crucial in observational studies. For this purpose, the marginal...
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
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...
Geodesic Optimal Transport Regression
Hans-Georg Müller, 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...
Comparing 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 ...
Structural restrictions in local causal discovery: identifying direct causes of a target variable
J Bodik, V Chavez-Demoulin
Abstract
Sparse higher order partial least squares for simultaneous variable selection, dimension reduction and tensor denoising
Kwangmoon Park, Sündüz Keleş
Abstract Motivated by the challenge of estimating effects of DNA methylation on 3D genomic contacts captured by multi-modal single c...
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...
Generalized Fréchet means with random minimizing domains and its strong consistency
Jaesung Park, Sungkyu Jung
Abstract This paper introduces a novel extension of Fréchet means, referred to as generalized Fréchet means, as a comprehensive fram...
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...
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...
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...
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 ...
Diaconis–Ylvisaker prior penalized likelihood for 𝒑/𝒏 → 𝜿 ∈ (0,1) logistic regression
P Sterzinger, I Kosmidis
Summary We characterize the behaviour of the maximum Diaconis–Ylvisaker prior penalized likelihood estimator in high-dimensional log...
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 ...
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...
Palm distributions of superposed point processes for statistical inference
M Beraha, F Camerlenghi, L Ghilotti
Abstract Palm distributions play a central role in the study of point processes and their associated summary statistics. In this wor...
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...
Dynamic clustering for heterophilic stochastic block models with time-varying node memberships
K Z Lin, J Lei
Summary We consider a time-ordered sequence of networks stemming from stochastic block models in which nodes gradually change their ...
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...
Sequential Gibbs Posteriors with Applications to Principal Component Analysis
David B Dunson, Steven Winter, Omar Melikechi
Abstract Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning p...
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...
Fast convergence of the Expectation-Maximization algorithm under a logarithmic Sobolev inequality
R CaprioandA M Johansen
Goodness-of-fit tests for linear non-Gaussian structural equation models
D Schkoda, M Drton
Abstract
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
Existence and Applications of Finite Population Samples that are Exactly Balanced
Yves Tillé, Louis-Paul Rivest
Abstract
Infinite joint species distribution models
D B Dunson, F Stolf
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 Radojičić, J Virta
Abstract
Factor pre-training in Bayesian multivariate logistic models
D B Dunson, L Mauri
Abstract
Decomposing Gaussians with Unknown Covariance
A Dharamshi, others
Abstract
A More Robust Approach to Multivariable Mendelian Randomization
Yinxiang Wu, 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 García-Portugués, 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 AdaptabilityGet access
Hanxuan YeandHongzhe Li
Multicalibration for Modeling Censored Survival Data with Universal Adaptability
Hanxuan Ye, Hongzhe Li
Abstract
Nonsense associations in Markov random fields with pairwise dependenceGet access
Sohom Bhattacharyaand others
Nonsense associations in Markov random fields with pairwise dependence
Sohom Bhattacharya, others
Abstract
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
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 ...
Testable implications of outcome-independent missingness not at random in covariates
A Sjölander, 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...
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 Metropolis–Hastings schemes, w...
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...
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...
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 ...
High-dimensional Factor Analysis for Network-linked data
Jinming Li, others
Abstract
‘On the behaviour of marginal and conditional AIC in linear mixed models’
Sonja Greven, Thomas Kneib