Prediction Intervals
94 papers with code • 0 benchmarks • 2 datasets
A prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis.
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Friedman1
aGender
Latest papers
Asymptotically free sketched ridge ensembles: Risks, cross-validation, and tuning
We also propose an "ensemble trick" whereby the risk for unsketched ridge regression can be efficiently estimated via GCV using small sketched ridge ensembles.
Conditional validity of heteroskedastic conformal regression
nmdwolf/heteroskedasticconformalregression
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This paper tries to shed new light on how prediction intervals can be constructed, using methods such as normalized and Mondrian conformal prediction, in such a way that they adapt to the heteroskedasticity of the underlying process.
Conformal prediction for frequency-severity modeling
We present a nonparametric model-agnostic framework for building prediction intervals of insurance claims, with finite sample statistical guarantees, extending the technique of split conformal prediction to the domain of two-stage frequency-severity modeling.
Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction
yongsukyee/uncertain_blackholemass
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In contrast to baseline approaches for prediction interval estimation, we show that the CQR method provides prediction intervals that adjust to the black hole mass and its related properties.
Integrating Uncertainty Awareness into Conformalized Quantile Regression
rrross/uacqr
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The reason is that the prediction intervals of CQR do not distinguish between two forms of uncertainty: first, the variability of the conditional distribution of $Y$ given $X$ (i. e., aleatoric uncertainty), and second, our uncertainty in estimating this conditional distribution (i. e., epistemic uncertainty).
Conformal Prediction with Missing Values
mzaffran/conformalpredictionmissingvalues
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This motivates our novel generalized conformalized quantile regression framework, missing data augmentation, which yields prediction intervals that are valid conditionally to the patterns of missing values, despite their exponential number.
On training locally adaptive CP
nicolorhul/ontraininglocalizedcp
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We address the problem of making Conformal Prediction (CP) intervals locally adaptive.
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification
The framework focuses on uncertainty quantification in complex data scenarios, particularly involving unstructured data while utilizing deep learning models.
Uncertainty Aware Neural Network from Similarity and Sensitivity
In the proposed NN training method for UQ, first, we train a shallow NN for the point prediction.
Design-based conformal prediction
valeman/awesome-conformal-prediction
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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.
