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A data analyst might worry about generalization if dropping a very small fraction of data points from a study could change its substantive conclusions.
Statistical Methods for Research Workers
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Frank R. Hampel · 1974
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R. Dennis Cook · 1977
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David A. Belsley, Edwin Kuh, and Roy E. Welsch · 1980
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Logistic Regression Diagnostics
Daryl Pregibon · 1981
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A predictive view of the detection and characterization of influential observations in regression analysis
Wesley Johnson and Seymour Geisser · 1983
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K-clustering as a detection tool for influential subsets in regression
J. Brian Gray and Robert F. Ling · 1984
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AC Atkinson · 1986
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Influential observations, high leverage points, and outliers in linear regression
Samprit Chatterjee and Ali S. Hadi · 1986
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Assessment of local influence
R Dennis Cook · 1986
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Sensitivity analysis in linear regression
Samprit Chatterjee and Ali S. Hadi · 1988
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Masking and swamping effects on tests for multiple outliers in normal sample
SM Bendre · 1989
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An expected utility approach to influence diagnostics
Bradley P. Carlin and Nicholas G. Polson · 1991
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Procedures for the identification of multiple outliers in linear models
Ali S. Hadi and Jeffrey S. Simonoff · 1993
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Deletion influence and masking in regression
A.J. Lawrance · 1995
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Case influence analysis in Bayesian inference
Eric T. Bradlow and Alan M. Zaslavsky · 1997
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Plastic phenotypic response to light of 16 congeneric shrubs from a Panamanian rainforest
Fernando Valladares, S. Joseph Wright, Eloisa Lasso, Kaoru Kitajima, and Robert W. Pearcy · 2000
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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Contradicted and initially stronger effects in highly cited clinical research
John P.A. Ioannidis · 2005
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Perturbation selection and influence measures in local influence analysis
Hongtu Zhu, Joseph G. Ibrahim, Sikyum Lee, and Heping Zhang · 2007
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Indirect effects of an aid program: how do cash transfers affect ineligibles’ consumption?
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Mostly harmless econometrics: An empiricist’s companion
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A protocol for data exploration to avoid common statistical problems
Alain F. Zuur, Elena N. Ieno, and Chris S. Elphick · 2010
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Approximate cross-validation in high dimensions with guarantees
William Stephenson and Tamara Broderick · 2020
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Hidden in plain sight: Influential sets in linear models
Nikolas Kuschnig, Gregor Zens, and Jesús Crespo Cuaresma · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh · 2021
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Covid-19 vaccine and post-pandemic recovery: Evidence from bitcoin cross-asset implied volatility spillover
Michael Di and Ke Xu · 2022
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Datamodels: Predicting predictions from training data
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Dean De Cock · 2011
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The Oregon health insurance experiment: evidence from the first year
Amy Finkelstein, Sarah Taubman, Bill Wright, Mira Bernstein, Jonathan Gruber, Joseph P. Newhouse, Heidi Allen, Katherine Baicker, and the Oregon Health Study Group · 2012
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Bayesian case influence measures for statistical models with missing data
Hongtu Zhu, Joseph G. Ibrahim, Hyunsoon Cho, and Niansheng Tang · 2012
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BAAD: a biomass and allometry database for woody plants
Robert A. York · 2016
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Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, and Vahid Tarokh · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
Sinisa Hrvatin, Daniel R. Hochbaum, M. Aurel Nagy, Marcelo Cicconet, Keiramarie Robertson, Lucas Cheadle, Rapolas Zilionis, Alex Ratner, Rebeca Borges-Monroy, Allon M. Klein, Bernardo L. Sabatini, and Michael E. Greenberg · 2018
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Learning GMMs with nearly optimal robustness guarantees
Allen Liu and Ankur Moitra · 2022
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How much should we trust the dictator’s GDP growth estimates?
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Algorithms that approximate data removal: New results and limitations
Vinith Suriyakumar and Ashia C. Wilson · 2022
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Negative externalities of temporary reductions in cognition: Evidence from particulate matter pollution and fatal car crashes
Anne M. Burton and Travis Roach · 2023
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Towards practical robustness auditing for linear regression
Daniel Freund and Samuel B. Hopkins · 2023
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Provably auditing ordinary least squares in low dimensions
Ankur Moitra and Dhruv Rohatgi · 2023
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TRAK: Attributing model behavior at scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry · 2023
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The use of linear models in quantitative research
Andrés F. Castro Torres and Aliakbar Akbaritabar · 2024
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Training microentrepreneurs over Zoom: Experimental evidence from Mexico
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Most influential subset selection: Challenges, promises, and beyond
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Using gradients to check sensitivity of MCMC-based analyses to removing data
Tin D. Nguyen, Ryan Giordano, Rachael Meager, and Tamara Broderick · 2024
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Robustness auditing for linear regression: To singularity and beyond
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