Statistical Science

Papers
(The median citation count of Statistical Science is 1. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-09-01 to 2024-09-01.)
ArticleCitations
A General Framework for Vecchia Approximations of Gaussian Processes89
The Box–Cox Transformation: Review and Extensions66
Exponential-Family Models of Random Graphs: Inference in Finite, Super and Infinite Population Scenarios52
Revisiting the Gelman–Rubin Diagnostic46
A Selective Overview of Deep Learning44
Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons38
The Dependent Dirichlet Process and Related Models30
Additive and Multiplicative Effects Network Models29
In Defense of the Indefensible: A Very Naïve Approach to High-Dimensional Inference27
The GENIUS Approach to Robust Mendelian Randomization Inference24
Analyzing Stochastic Computer Models: A Review with Opportunities21
Robust High-Dimensional Factor Models with Applications to Statistical Machine Learning20
Convex Relaxation Methods for Community Detection18
A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery18
Game-Theoretic Statistics and Safe Anytime-Valid Inference16
Maximum Likelihood Multiple Imputation: Faster Imputations and Consistent Standard Errors Without Posterior Draws15
Testing Randomness Online15
Choosing Among Notions of Multivariate Depth Statistics15
Bipartite Causal Inference with Interference15
Sparse Regression: Scalable Algorithms and Empirical Performance14
Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing13
Aitchison’s Compositional Data Analysis 40 Years on: A Reappraisal13
Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss and Simpson’s Paradox12
Stein’s Method Meets Computational Statistics: A Review of Some Recent Developments12
A General Framework for the Analysis of Adaptive Experiments12
Challenges in Markov Chain Monte Carlo for Bayesian Neural Networks11
Response-Adaptive Randomization in Clinical Trials: From Myths to Practical Considerations11
A Horse Race between the Block Maxima Method and the Peak–over–Threshold Approach10
Statistical Dependence: Beyond Pearson’s ρ10
A Problem in Forensic Science Highlighting the Differences between the Bayes Factor and Likelihood Ratio10
Symmetrical and Non-symmetrical Variants of Three-Way Correspondence Analysis for Ordered Variables10
On Estimation and Inference in Latent Structure Random Graphs9
Statistical Modeling for Practical Pooled Testing During the COVID-19 Pandemic9
Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion9
Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review8
Methods to Compute Prediction Intervals: A Review and New Results7
A Look at Robustness and Stability of $\ell_{1}$-versus $\ell_{0}$-Regularization: Discussion of Papers by Bertsimas et al. and Hastie et al.7
Power Calculations for Replication Studies7
On General Notions of Depth for Regression7
Identification of Causal Effects Within Principal Strata Using Auxiliary Variables6
Confidence as Likelihood6
The SPDE Approach to Matérn Fields: Graph Representations6
Statistical Challenges in Tracking the Evolution of SARS-CoV-26
Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality6
Gambler’s Ruin and the ICM6
Principal Fairness for Human and Algorithmic Decision-Making6
Modern Bayesian Experimental Design5
Network Modeling in Biology: Statistical Methods for Gene and Brain Networks5
Approximating Bayes in the 21st Century5
Confidence and Discoveries with E-values5
Additive Bayesian Variable Selection under Censoring and Misspecification5
The Covariate-Adjusted ROC Curve: The Concept and Its Importance, Review of Inferential Methods, and a New Bayesian Estimator5
Local scale invariance and robustness of proper scoring rules5
Being a Public Health Statistician During a Global Pandemic4
A Statistical Framework for Modern Network Science4
Stochastic Approximation: From Statistical Origin to Big-Data, Multidisciplinary Applications4
Intention-to-Treat Comparisons in Randomized Trials4
Bayesian Analysis of Rank Data with Covariates and Heterogeneous Rankers3
The Matérn Model: A Journey Through Statistics, Numerical Analysis and Machine Learning3
Confidence Intervals for Seroprevalence3
A Regression Perspective on Generalized Distance Covariance and the Hilbert–Schmidt Independence Criterion3
The Poisson Binomial Distribution— Old & New3
High-Performance Statistical Computing in the Computing Environments of the 2020s3
Statistical Aspects of the Quantum Supremacy Demonstration3
Replicability Across Multiple Studies3
Seven Principles for Rapid-Response Data Science: Lessons Learned from Covid-19 Forecasting3
Online Multiple Hypothesis Testing3
Rejoinder: Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons3
Discussion of “Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons”3
Past, Present and Future of Software for Bayesian Inference2
Computing Bayes: From Then ‘Til Now2
Cross-Study Replicability in Cluster Analysis2
A Discussion on Practical Considerations with Sparse Regression Methodologies2
Distributed Bayesian Inference in Massive Spatial Data2
Interpreting p-Values and Confidence Intervals Using Well-Calibrated Null Preference Priors2
Comment: Settle the Unsettling: An Inferential Models Perspective2
Diffusion Smoothing for Spatial Point Patterns2
Comment: On Focusing, Soft and Strong Revision of Choquet Capacities and Their Role in Statistics2
Khinchin’s 1929 Paper on Von Mises’ Frequency Theory of Probability2
Studentization Versus Variance Stabilization: A Simple Way Out of an Old Dilemma1
Measurement Error Models: From Nonparametric Methods to Deep Neural Networks1
A Comparative Tour through the Simulation Algorithms for Max-Stable Processes1
Approximate Confidence Intervals for a Binomial p—Once Again1
Distributionally Robust and Generalizable Inference1
Data Science in a Time of Crisis: Lessons from the Pandemic1
Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity1
Variational Inference for Cutting Feedback in Misspecified Models1
30 Years of Synthetic Data1
Bayesian Adaptive Randomization with Compound Utility Functions1
Modern Variable Selection in Action: Comment on the Papers by HTT and BPV1
Modeling the Occurrence of Events Subject to a Reporting Delay via an EM Algorithm1
Comment: Response Adaptive Randomization in Practice1
A Hybrid Scan Gibbs Sampler for Bayesian Models with Latent Variables1
A Conversation with Tze Leung Lai1
In Praise (and Search) of J. V. Uspensky1
On Some Connections Between Esscher’s Tilting, Saddlepoint Approximations, and Optimal Transportation: A Statistical Perspective1
The Secret Life of I. J. Good1
Comment: Moving Beyond Sets of Probabilities1
Rejoinder: Sparse Regression: Scalable Algorithms and Empirical Performance1
Experimental Design in Marketplaces1
Can We Reliably Detect Biases that Matter in Observational Studies?1
Comment: A Quarter Century of Methodological Research in Response-Adaptive Randomization1
Replication Success Under Questionable Research Practices—a Simulation Study1
Some Perspectives on Inference in High Dimensions1
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