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Conformal prediction is an assumption-lean approach to generating distribution-free prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite-sample coverage.
Survey design under the regression superpopulation model
Isaki, C. T. and Fuller, W. A. (1982) · 1982
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Model Assisted Survey Sampling
Särndal, C.-E., Swensson, B., and Wretman, J. (1992) · 1992
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Adaptive sampling in behavioral surveys
Thompson, S. K. (1997) · 1997
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Inductive confidence machines for regression
Papadopoulos, H., Proedrou, K., Vovk, V., and Gammerman, A. (2002) · 2002
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The Enhanced Forest Inventory and Analysis Program—National Sampling Design and Estimation Procedures
Bechtold, W. A. and Patterson, P. L. (2005) · 2005
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The forest vegetation simulator: a review of its structure, content, and applications
Crookston, N. L. and Dixon, G. E. (2005) · 2005
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On the two-phase framework for joint model and design-based inference
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Classification with valid and adaptive coverage
Romano, Y., Sesia, M., and Candes, E. J. (2020) · 2006
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Generalized variance functions
Wolter, K. (2007) · 2007
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The relevance or irrelevance of weights for confidentiality and statistical analyses
Fienberg, S. E. (2010) · 2010
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Prediction of functional status for the elderly based on a new ordinal regression model
Hong, H. G. and He, X. (2010) · 2010
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Conditional validity of inductive conformal predictors
Vovk, V. (2013) · 2013
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The Dirichlet-Multinomial and Dirichlet-Categorical models for Bayesian inference
Tu, S. (2014) · 2014
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Medical Expenditure Panel Survey 2015 full year consolidated data file
Agency for Healthcare Research and Quality (2017) · 2015
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Living Conditions Monitoring Survey (LCMS)
Central Statistical Office, Zambia (2015) · 2015
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The Second Longitudinal Study of Aging (LSOA II)
National Center for Health Statistics (2016) · 2016
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Household poverty classification in data-scarce environments: A machine learning approach
Kshirsagar, V., Wieczorek, J., Ramanathan, S., and Wells, R. (2017) · 2017
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Fitting regression models to survey data
Lumley, T. and Scott, A. (2017) · 2017
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Model-assisted survey regression estimation with the lasso
McConville, K. S., Breidt, F. J., Lee, T., and Moisen, G. G. (2017) · 2017
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Academic Performance Index
California Department of Education (2018) · 2018
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Exact and robust conformal inference methods for predictive machine learning with dependent data
Chernozhukov, V., Wüthrich, K., and Yinchu, Z. (2018) · 2018
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Distribution-free predictive inference for regression
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2018) · 2018
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Probability: Theory and Examples
Durrett, R. (2019) · 2019
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Predicting understory vegetation structure in selected western forests of the United States using FIA inventory data
Krebs, M. A., Reeves, M. C., and Baggett, L. S. (2019) · 2019
Sampling: Design and Analysis
Lohr, S. L. (2021) · 2021
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Design-unbiased statistical learning in survey sampling
Sande, L. S. and Zhang, L.-C. (2021) · 2021
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Conformal prediction using conditional histograms
Sesio, M. and Romano, Y. (2021) · 2021
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A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Angelopoulos, A. N. and Bates, S. (2022) · 2022
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Efficient and differentiable conformal prediction with general function classes
Bai, Y., Mei, S., Wang, H., Zhou, Y., and Xiong, C. (2022) · 2022
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Conformalized quantile regression
Romano, Y., Patterson, E., and Candes, E. (2019) · 2019
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Conformal prediction under covariate shift
Tibshirani, R. J., Barber, R. F., Candes, E., and Ramdas, A. (2019) · 2019
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Nearest neighbor imputation for general parameter estimation in survey sampling
Yang, S. and Kim, J. K. (2019) · 2019
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How the Washington Post estimates outstanding votes for the 2020 presidential election
Cherian, J. and Bronner, L. (2020) · 2020
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A comparison of some conformal quantile regression methods
Sesio, M. and Candes, E. J. (2020) · 2020
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The limits of distribution-free conditional predictive inference
Barber, R. F., Candes, E. J., Ramdas, A., and Tibshirani, R. J. (2021) · 2021
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Barber, R. F., Candes, E. J., Ramdas, A., and Tibshirani, R. J. (2022) · 2022
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Optimal conformal prediction for small areas
Bersson, E. and Hoff, P. D. (2022) · 2022
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Model-assisted estimation in high-dimensional settings for survey data
Dagdoug, M., Goga, C., and Haziza, D. (2022) · 2022
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Distribution-free prediction sets for two-layer hierarchical models
Dunn, R., Wasserman, L., and Ramdas, A. (2022) · 2022
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Conformal prediction under feedback covariate shift for biomolecular design
Fannjiang, C., Bates, S., Angelopoulos, A. N., Listgarten, J., and Jordan, M. I. (2022) · 2022
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Awesome conformal prediction
Manokhin, V. (2022) · 2022
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Split conformal prediction for dependent data
Oliveira, R. I., Orenstein, P., Ramos, T., and Romano, J. V. (2022) · 2022
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Methods to compute prediction intervals: A review and new results
Tian, Q., Nordman, D. J., and Meeker, W. Q. (2022) · 2022
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Algorithmic Learning in a Random World
Vovk, V., Gammerman, A., and Shafer, G. (2022) · 2022
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K-fold cross-validation for complex sample surveys
Wieczorek, J., Guerin, C., and McMahon, T. (2022) · 2022
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Testing for outliers with conformal p-values
Bates, S., Candès, E., Lei, L., Romano, Y., and Sesia, M. (2023) · 2023
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On the validity of conformal prediction for network data under non-uniform sampling
Lunde, R. (2023) · 2023
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