Paper proposes a novel method to test differences in spatial point patterns.
problem Detecting differences in the first-order structures of spatial point patterns.
method Kernel mean embedding with approximate version tailored for spatial point processes, reducing comparison to Euclidean space t-tests.
result The proposed method is powerful and well-calibrated, demonstrated on real-world data.
A new method selects variables for random survival forests using maximally selected rank statistics.
problem Random survival forests can be biased in selecting variables, especially for non-linear effects.
method Use maximally selected rank statistics for variable selection in random survival forests, comparing on p-value scale.
result The new method outperforms other approaches in prediction performance and computational speed.
We propose a differentiable sigmoid function for efficient p-value calculation in clustering.
problem Efficient and accurate p-value calculation for clustering algorithms.
method Designed a differentiable sigmoid function to approximate the Dip-p-value transformation.
result Accelerates computation and integrates well with gradient descent-based learning schemes.
New method converts p-values to e-values for more efficient CP and aggregation.
problem Limitations of existing p-to-e calibrators in CP setting.
method Proposes a novel P2E calibrator for set-preserving calibration.
result Significant efficiency gains over existing p-to-e calibrators.
P-value hacking can produce misleadingly low p-values, skewing meta-analysis results.
problem Misleading p-values in meta-analysis due to p-value hacking.
method Deriving the meta-distribution for p-values and analyzing the power of tests.
result Minimum p-values can be significantly lower than the true p-value, skewing results.
New method tests Granger non-causality in panel data with cross-sectional dependencies.
problem Testing Granger non-causality in panel data with cross-sectional dependencies.
method Proposes a new approach to aggregate p-values from panel members to test Granger non-causality, showing lower FDR.
result Our approach discovers true causal relations in panel data, unlike state-of-the-art methods.
Valid p-value for bounded random variables without distributional assumptions.
problem Calibration of predictive algorithms in a distribution-free setting.
method Built a super-uniform p-value based on a concentration inequality.
result Super-uniform p-value is tighter than existing alternatives.
A method uses neural networks to approximate sampling distributions of test statistics.
problem Accurate modeling of p-value functions or cdfs for correct confidence set coverage.
method Uses neural networks to model the cdf of test statistics, approximating sampling distributions.
result Neural network approximations of sampling distributions are effective and simple.
Estimates peeking effects in p-values to correct bias.
problem Data peeking biases reported p-values downward.
method Develops mechanisms to estimate running extrema of test statistics.
result Corrects bias in p-values due to peeking.
A new algorithm estimates and controls FDR for PC algorithm edges.
problem Estimating and controlling FDR for PC algorithm edges.
method PC-p algorithm using edge-specific p-values and Benjamini-Yekutieli FDR procedure.
result PC-p yields more accurate FDR estimation and control.
The paper introduces localized conformal p-values for conditional testing problems.
problem Addressing conditional testing problems in statistics.
method Localized conformal p-values defined by inverting prediction intervals.
result Proposes procedures for conditional outlier detection and label screening with FDR and FWER control.
The paper develops p-values for outlier detection using conformal inference.
problem Detecting outliers in nonparametric data sets.
method Conformal inference framework for constructing marginally valid but mutually dependent p-values.
result Valid p-values for outlier detection with conditional independence and marginal false discovery rate control.
New theorem for deep neural networks improves classification margins.
problem Improving classification margins in deep neural networks.
method Local class-purity theorem and margin p-values for training and testing samples.
result Enhanced understanding and computation of classification margins.
This paper studies the geometry of minimum-volume confidence sets for multinomial parameters.
problem Determining if minimum-volume confidence sets for multinomial outcomes are disjoint.
method Enumerating and covering the continuous regions of the exact p-value function to study the geometry of minimum-volume confidence sets.
result The geometry of minimum-volume confidence sets for multinomial parameters is studied, providing insights into their structure and properties.
New calibration measures for multi-class classification improve model accuracy.
problem Calibration of multi-class classification models is insufficient for safety-critical applications.
method Developed new calibration measures and estimators for multi-class classification.
result Proposed estimators improve interpretability and accuracy of calibration measures.
Many model selection algorithms produce a path of fits specifying a sequence of increasingly complex models. Given such a sequence and the data used to produce them, we consider the problem of choosing the least complex model that is not falsified by the data. Extending the selected-model tests of Fithian et al. (2014)…
Structural equation models and Bayesian networks have been widely used to study causal relationships between continuous variables. Recently, a non-Gaussian method called LiNGAM was proposed to discover such causal models and has been extended in various directions. An important problem with LiNGAM is that the results a…
This paper improves prediction accuracy for multi-input classification tasks using p-value aggregation.
problem Generating accurate predictive sets with guaranteed coverage for multi-input classification tasks.
method Integrates p-values from each observation to reduce the size of the predicted label set while maintaining class-conditional coverage.
result The method reduces the size of the predicted label set while preserving the required coverage guarantee.
Conformal Test Martingales can be 'blind' to significant changes in data distribution.
problem The converse of exchangeability does not hold, leading to potential blindness of CTMs.
method Explicit construction of A-cryptic change-point using bivariate Gaussian distributions. result CTMs can be perfectly cryptic to a significant change in marginal means.
Unified Bayesian framework improves clinical trial hypothesis testing.
problem Lack of transparency and inability to quantify evidence in traditional P-values.
method Interval null hypothesis framework combined with Bayes factor-based tests.
result Bayesian interval hypothesis testing ensures frequentist error control and interpretability.
A communication-efficient method controls FDR in network settings.
problem Controlling FDR in networks with limited communication.
method Sample-and-Forward: a flexible procedure for multihop networks.
result Nodes can control FDR without sharing p-values, achieving power and FDR control.
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two pa…
Proposes a method to quantify the reliability of salient regions in deep learning models using p-values.
problem Difficulty in assessing the reliability of saliency maps generated by deep learning models.
method Proposes a selective inference framework to quantify the reliability of salient regions as selected hypotheses by deep learning models.
result The method can provably control the probability of false positive detections of salient regions.
A method selects candidates based on predictions with statistical control.
problem Screening candidates for resource-intensive steps like hiring or drug discovery.
method Wraps around any prediction model to produce a subset of candidates with controlled false selection rate.
result Empirically demonstrates selection of candidates whose predictions exceed a data-dependent threshold.
The paper confirms two groups of gamma-ray bursts using a new nonparametric metric.
problem Determining the number of inherent groups in gamma-ray bursts.
method A new nonparametric interpoint distance-based measure, combined with clustering methods.
result Confirms two groups of short and long gamma-ray bursts.
Novel framework provides statistical significance for image segmentation results.
problem Evaluating the reliability of individual image segmentation results.
method Selective inference to account for segmentation bias in p-value computation.
result Valid p-values for segmentation results, accounting for bias.
The likelihood ratio test in high-dimensional logistic regression is not a chi-square, but a rescaled one.
problem The incorrect chi-square approximation in high-dimensional logistic regression.
method Proving the rescaled chi-square distribution and solving nonlinear equations.
result The likelihood ratio test is a rescaled chi-square, not a standard chi-square.
Alternative to p-values using machine learning techniques.
problem Traditional p-values in regression settings.
method Leave-one-out bootstrap for prediction error, modified to measure variable importance.
result VIMP index provides interpretable measure of variable effect size.
E-values enhance conformal prediction methods.
problem Distribution-free uncertainty quantification.
method Reformulation of conformal prediction using e-values.
result E-values offer new theoretical and practical capabilities.
Logistic regression for brain imaging without p-values.
problem Computing the distribution of random field suprema is hard.
method Uses logistic regression for brain network classification.
result Performs classification at each edge level without preselected features.
Two new tests approximate KCIT for fast CI testing in large datasets.
problem CI testing is slow and resource-intensive for large datasets.
method Developed RCIT and RCoT, which approximate KCIT using random Fourier features.
result RCIT and RCoT scale linearly with sample size and return accurate p-values faster than KCIT.
This paper introduces a new method to compare collections of distributions on manifolds and graphs.
problem Comparing collections of probability distributions over diverse domains.
method Intrinsic slicing construction for Wasserstein distances, Hilbert embedding, resampling, p-value combination.
result Powerful and well-calibrated p-values for comparing distributions on manifolds and graphs.
Let (X,Y) be a random variable consisting of an observed feature vector X∈X and an unobserved class label Y∈{1,2,...,L} with unknown joint distribution. In addition, let D be a training data set consisting of n completely observed independent copies of (X,Y). Usual classification…
Paper extends SI method for detecting CPs in complex systems' frequency domain.
problem Identifying change points in complex systems' frequency domain.
method Extends SI framework to frequency domain using DFT properties and develops valid p-values.
result Reliable detection of genuine CPs with strong statistical guarantees.
The paper develops exact and approximate conformal inference methods for multi-output regression.
problem Uncertainty quantification in multi-output regression predictions.
method Exact derivations and approximations of conformal inference p-values for linear multi-output predictors, and efficient methods for nonlinear predictors.
result Efficient methods for approximating conformal prediction regions for multi-output predictors, both linear and nonlinear.
Novel conformal methods test out-of-distribution data with labeled outliers.
problem Testing whether new data comes from the same distribution as a reference.
method Integrative conformal p-values re-weight standard p-values using dependent side information.
result The methods outperform standard conformal p-values in simulations and applications.
OptCS optimizes model selection after conformal inference, controlling FDR and power loss.
problem Challenges in model selection for conformal inference, especially when limited labeled data and many model choices are available.
method OptCS framework that allows valid statistical testing after flexible data-driven model optimization, using novel multiple testing procedures.
result Valid conformal p-values constructed despite substantial data reuse, maintaining FDR control.
Solves capillary curvature problems for specific p values.
problem Capillary curvature problems for −n<p<1 and θ∈(0,2π). method Iterative scheme based on capillary Minkowski problem and capillary curvature image operators.
result Fixed points of capillary curvature image operators correspond to solutions of capillary Lp-Minkowski problem. Estimating the dependences between random variables, and ranking them accordingly, is a prevalent problem in machine learning. Pursuing frequentist and information-theoretic approaches, we first show that the p-value and the mutual information can fail even in simplistic situations. We then propose two conditions for r…
Method predicts rarity of image features to support research integrity investigations.
problem Difficulty in determining if image reuse is by chance or intentional.
method Statistical estimation of ORB features' chance occurrence across PubMed Open Access Subset dataset.
result The method produces decreasingly smaller p-values for more complex imagery, supporting null hypothesis.
Unified theoretical guarantees for distribution-free changepoint detection and testing.
problem Distribution-free changepoint inference with finite-sample validity and consistency.
method Distribution-free changepoint localization using conformal p-values with theoretical guarantees.
result Unified distribution-free guarantees for changepoint detection, localization, and testing.
New algorithms predict causal links better than traditional methods in time series data.
problem Learning causal structure from time series data with challenges in real-world Earth sciences.
method Combination of established ideas for linear methods to identify causal links in non-linear systems, with a focus on large regression coefficients.
result Large regression coefficients can predict causal links better than small p-values in practice.
A new framework for brain mapping using statistical agnostic methods.
problem Estimating brain connectivity with limited data and controlling false positives.
method Statistical Agnostic Mapping (SAM) based on concentration inequalities.
result Relieves instability and provides less conservative p-value correction.
Paper introduces a method to assess the statistical reliability of changepoints using selective inference and dynamic programming.
problem Assessing the statistical reliability of detected changepoints.
method Selective inference framework combined with dynamic programming for exact p-value computation.
result Proposes a method with high statistical power and decent computational efficiency.
LASLA improves multiple testing accuracy with network-structured data.
problem Efficiently incorporating complex side information into multiple testing.
method Locally adaptive structure learning algorithm (LASLA) integrating auxiliary information.
result LASLA asymptotically controls FDR and enhances power with side information.
CERN improves activity recognition in videos with novel energy layer and p-values.
problem Recognizing group activities in videos at semantic levels.
method Two-level LSTM network with Confidence-Energy Recurrent Network (CERN).
result Superior performance compared to state-of-the-art approaches.
Python package 'nonconform' simplifies conformal anomaly detection.
problem Heuristic thresholding in anomaly detection systems.
method 'nonconform' package converts anomaly scores into calibrated p-values.
result Statistically principled anomaly detection is made accessible.
Randomized hierarchical clustering tests for stability and detects clusters.
problem Greedy hierarchical clustering's sensitivity to data perturbations.
method Randomization scheme and p-values at each node.
result Valid hypothesis testing procedures for clustering results.