The problem of multiple hypothesis testing arises when there are more than one hypothesis to be tested simultaneously for statistical significance. This is a very common situation in many data mining applications. For instance, assessing simultaneously the significance of all frequent itemsets of a single dataset entai…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,657 papers · 148 categories
Trend · papers per month
48 results for “multiple hypothesis testing”
NeurT-FDR controls FDR by incorporating auxiliary covariates in deep learning.
problem Controlling FDR in complex large-scale problems with indirect relations among covariates.
method NeurT-FDR uses a deep Black-Box framework that parametrizes test-level covariates as a neural network and adjusts auxiliary covariates through a regression framework.
result NeurT-FDR makes substantially more discoveries in real datasets compared to competitive baselines.
Formula derived for sample complexity in binary hypothesis testing.
problem Determine the minimum number of samples to distinguish between two distributions.
method Developed a formula for sample complexity in both prior-free and Bayesian settings, using Jensen-Shannon and Hellinger divergences.
result Formula characterizes sample complexity for a wide range of error parameters, up to multiplicative constants.
In hypothesis testing, a false discovery occurs when a hypothesis is incorrectly rejected due to noise in the sample. When adaptively testing multiple hypotheses, the probability of a false discovery increases as more tests are performed. Thus the problem of False Discovery Rate (FDR) control is to find a procedure for…
NeurT-FDR controls FDR by incorporating feature hierarchy.
problem Controlling FDR in complex, large-scale hypothesis testing problems.
method NeurT-FDR uses a neural network to parametrize test-level covariates and a regression framework to adjust feature hierarchy.
result NeurT-FDR makes substantially more discoveries than competitive baselines.
Calibration without labels in multiple testing
problem Interpretable error probabilities in large-scale hypothesis testing
method Constructing pseudo-labels from spacings of ordered -values
result Finding that -value can be severely miscalibrated
Framework for online hypothesis testing across various data types.
problem Testing various nonparametric hypotheses in data streams.
method Unified framework using operators on data distributions, leveraging ML models.
result Efficient, adaptive, and error-controlled sequential tests.
Study on continuous sequence classification with distribution uncertainty.
problem Classifying continuous sequences with varying distribution uncertainty.
method Proposes distribution-free tests for three test designs: fixed-length, sequential, and two-phase tests.
result Error probabilities decay exponentially fast for all test designs.
Improved hypothesis testing and change-point detection using diffusion-based methods.
problem Limited power of score-based hypothesis tests and change-point detection.
method Extending score-based Fisher divergence to diffusion-divergence by multiplying score functions with a matrix-valued function or weight matrix.
result Theoretical quantification and demonstration of optimal performance of diffusion-based algorithms.
aLTT selects hyperparameters efficiently with statistical guarantees.
problem Statistical validity and efficiency in hyperparameter selection.
method Sequential data-dependent multiple hypothesis testing with early termination.
result Reduces testing rounds while maintaining statistical validity.
New framework compares credal sets for hypothesis testing with epistemic uncertainty.
problem Comparing distributions with partial ignorance and epistemic uncertainty.
method Credal two-sample testing framework for convex sets of probability measures.
result Direct integration of epistemic uncertainty in hypothesis testing.
PEAK tests means of multiple data streams with sequential betting.
problem Testing means of multiple data streams with nonparametric methods.
method Sequential, nonparametric testing using a betting scheme.
result PEAK provides up to 85% reduction in samples for stopping.
A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on…
Unified framework controls false discovery rate in bandit multiple testing.
problem Designing adaptive algorithms to identify true discoveries in multiple hypothesis testing.
method Unified modular framework using e-processes for FDR control in arbitrary settings.
result Unified framework ensures FDR control for dependent and simultaneous arm queries.
Current statistical inference problems in areas like astronomy, genomics, and marketing routinely involve the simultaneous testing of thousands -- even millions -- of null hypotheses. For high-dimensional multivariate distributions, these hypotheses may concern a wide range of parameters, with complex and unknown depen…
GAIF enhances online multiple testing with feedback, improving statistical power.
problem Sequential online multiple testing with delayed feedback.
method GAIF framework using dynamic threshold adjustment and feedback-driven model selection.
result Improves statistical power through feedback-driven model selection.
AdaPT-GMM improves multiple testing power with covariates.
problem Powerful and robust multiple testing with covariates.
method Covariate-assisted Gaussian mixture model with adaptive thresholding.
result AdaPT-GMM delivers high power in various scenarios.
Max-rank improves multiple testing in conformal prediction.
problem Simultaneous testing of multiple hypotheses in scientific inquiries.
method Introduces max-rank, a novel correction for positive dependencies in simultaneous testing.
result Max-rank efficiently controls family-wise error rate and improves predictive uncertainty estimates.
Do two data samples come from different distributions? Recent studies of this fundamental problem focused on embedding probability distributions into sufficiently rich characteristic Reproducing Kernel Hilbert Spaces (RKHSs), to compare distributions by the distance between their embeddings. We show that Regularized Ma…
Power of network tests degrades when vertices are misaligned.
problem Power loss in network hypothesis testing due to vertex shuffling.
method Theoretical analysis and simulations of Frobenius norm differences in random dot product and stochastic block models.
result Shuffling vertices can significantly reduce the power of network tests.
Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model which jointly analyzes the cross-validation res…
Multiple hypothesis testing, a situation when we wish to consider many hypotheses, is a core problem in statistical inference that arises in almost every scientific field. In this setting, controlling the false discovery rate (FDR), which is the expected proportion of type I error, is an important challenge for making …
Framework calibrates ML models for risk control in various tasks.
problem Achieving statistical guarantees for model predictions.
method Reframing risk control as multiple hypothesis testing, applying statistical techniques.
result New calibration methods for multi-label classification, instance segmentation, outlier detection, and confidence set coverage.
As datasets grow richer, an important challenge is to leverage the full features in the data to maximize the number of useful discoveries while controlling for false positives. We address this problem in the context of multiple hypotheses testing, where for each hypothesis, we observe a p-value along with a set of feat…
DART2 enhances multiple testing by leveraging ancillary information robustly.
problem Enhancing multiple testing power with uncertain ancillary information.
method Distance-assisted multiple testing procedure (DART2) that handles both helpful and misleading ancillary information.
result DART2 asymptotically controls FDR and improves power when ancillary information is helpful, maintaining FDR and power otherwise.
A framework for hypothesis testing on attributed graphs using sampling.
problem Statistical testing on graph data, especially large attributed graphs.
method Sampling-based framework with PHASE and PHASEopt for accurate and efficient hypothesis testing.
result PHASE and PHASEopt improve accuracy and efficiency of hypothesis testing in attributed graphs.
This paper considers a multiple regression model and compares, under full model hypothesis, analytically as well as by simulation, the performance characteristics of some popular penalty estimators such as ridge regression, LASSO, adaptive LASSO, SCAD, and elastic net versus Least Squares Estimator, restricted estimato…
In this work, we consider hypothesis testing and anomaly detection on datasets where each observation is a weighted network. Examples of such data include brain connectivity networks from fMRI flow data, or word co-occurrence counts for populations of individuals. Current approaches to hypothesis testing for weighted n…
When Features Beat Noise: A Feature Selection Technique Through Noise-Based Hypothesis Testingstat.ML
A novel feature selection method using noise-based hypothesis testing improves feature selection accuracy.
problem Challenges in feature selection for complex, high-dimensional datasets.
method Introduces multiple random noise features and evaluates feature importance against noise feature maxima using non-parametric bootstrap-based hypothesis testing.
result Outperforms existing methods in simulated and real-world datasets.
The paper sets thresholds for testing correlation in hypergraphs, distinguishing between independent and correlated states.
problem Testing correlation between two hypergraphs under different models.
method Derives sharp information-theoretic thresholds for distinguishing between null and alternative hypotheses.
result The testing threshold decreases as the hypergraph's uniformity (m) increases, making correlation testing easier for higher uniformity.
Proposes a framework for OOD detection combining multiple statistics.
problem Detecting out-of-distribution (OOD) samples reliably during inference.
method Multiple hypothesis testing with conformal p-values.
result Uniformly outperforms threshold-based tests across different datasets and neural networks.
DBPA assesses LLM perturbations using frequentist hypothesis testing.
problem Quantifying input perturbation impacts on LLM outputs.
method DBPA reformulates perturbation analysis as frequentist hypothesis testing, using Monte Carlo sampling for empirical null and alternative distributions.
result DBPA provides interpretable p-values and scalar effect sizes for LLM perturbations.
Study robust hypothesis testing under Hellinger distance, proving lower bounds and providing tests.
problem Testing close variants of specified distributions robustly to Hellinger distance.
method Lower bound on slack factor, testing with Hellinger balls, symmetric chi-squared distance analysis.
result Lower bound on slack factor quantifies robustness under misspecification.
The statistical comparison of multiple algorithms over multiple data sets is fundamental in machine learning. This is typically carried out by the Friedman test. When the Friedman test rejects the null hypothesis, multiple comparisons are carried out to establish which are the significant differences among algorithms. …
The Sample Complexity of Distributed Simple Binary Hypothesis Testing under Information Constraintscs.IT
Paper resolves open problems on sample complexity in binary hypothesis testing.
problem Open problems in distributed simple binary hypothesis testing under information constraints.
method One-shot lower bound on Bayes error, streamlined sample complexity formula, reverse data-processing inequality.
result Optimally tight sample complexity bounds for communication-constrained simple binary hypothesis testing.
In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments. In such high-stakes tasks, false discoveries may lead investigators astray. These applications would therefore benefit from control over the finite-sample error rate of interpretations…
Finite-Sample Properties of Model Specification Tests for Multivariate Dynamic Regression Modelsecon.EM
Proposes a new test for validating multivariate dynamic regression models.
problem Inadequate exogeneity conditions for conventional model specification tests in dynamic systems.
method Develops a generalized Durbin estimator for multiple-equation systems with dynamic dependencies, and constructs Wald tests.
result Bootstrap-based Wald tests improve finite-sample size control and validate the null hypothesis in multifactor models.
Transforms any test into anytime-valid with sample savings.
problem Sequential data invalidates classical test guarantees.
method Predicts test outcomes to create anytime-valid stopping rules.
result Ensures Type-I error control and near-optimal power.
Paper proposes a new framework for hypothesis testing in imaging.
problem Challenges in hypothesis testing for imaging data.
method Combines self-supervised imaging, vision-language models, and non-parametric hypothesis testing.
result Demonstrates improved power and robust error control in image-based phenotyping.
Paper proposes a robust hypothesis testing method using Sinkhorn distance.
problem Hypothesis testing for small samples.
method Data-driven approach using Sinkhorn uncertainty sets.
result The method provides a more flexible detector compared to Wasserstein robust test.
Study hypothesis testing under quantized samples with communication constraints, achieving near-optimal sample complexity.
problem Optimizing hypothesis testing with quantized samples and communication constraints.
method Developed a polynomial-time algorithm achieving near-optimal sample complexity under communication constraints.
result Achieved near-optimal sample complexity under communication constraints, with a logarithmic factor increase over unconstrained setting.
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Sizestat.ML
New private algorithm for sequential hypothesis testing with privacy and error rate guarantees.
problem Privacy protection in sequential hypothesis testing for sensitive data.
method Renyi differential privacy, Wald's Sequential Probability Ratio Test (SPRT).
result Private algorithm with strong privacy guarantees and theoretical performance analysis.
Robust test for distributions under Hellinger distance, simpler than optimal tests.
problem Testing and estimating distributions robustly under Hellinger distance.
method Simple robust hypothesis test with optimal sample complexity, robust to Hellinger distance perturbations.
result Empirically demonstrated robustness and power of the test on canonical distributions.
ElbowSig assesses clustering structure at multiple scales.
problem Selecting optimal number of clusters in unsupervised learning.
method Formalizes elbow heuristic with a normalized discrete curvature statistic.
result Validates multiscale clustering structure over various resolutions.
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.
SynthBH uses synthetic data to control FDR in multiple testing.
problem Controlling false discovery rate in multiple hypothesis testing.
method SynthBH, a synthetic-powered multiple testing procedure.
result SynthBH guarantees FDR control with synthetic data.
We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of exist…
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.