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.
Sequential tests for nonparametric hypotheses using supermartingales.
Hypothesis testing in singular models is fundamentally about identifiable vs. non-identifiable parameters.
Near-optimal private tests for simple and MLR hypotheses developed under Gaussian differential privacy.
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…
There is a significant literature on methods for incorporating knowledge into multiple testing procedures so as to improve their power and precision. Some common forms of prior knowledge include (a) beliefs about which hypotheses are null, modeled by non-uniform prior weights; (b) differing importances of hypotheses, m…
New framework for valid hypothesis testing in complex data settings.
Drawing on some recent results that provide the formalism necessary to definite stationarity for infinite random graphs, this paper initiates the study of statistical and learning questions pertaining to these objects. Specifically, a criterion for the existence of a consistent test for complex hypotheses is presented,…
Paper proposes a new framework for hypothesis testing in imaging.
Private online FDR control for adaptive testing under differential privacy.
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…
We consider the problem of asynchronous online testing, aimed at providing control of the false discovery rate (FDR) during a continual stream of data collection and testing, where each test may be a sequential test that can start and stop at arbitrary times. This setting increasingly characterizes real-world applicati…
The problem of finding itemsets that are statistically significantly enriched in a class of transactions is complicated by the need to correct for multiple hypothesis testing. Pruning untestable hypotheses was recently proposed as a strategy for this task of significant itemset mining. It was shown to lead to greater s…
A new framework for private Bayesian tests maintains interpretability and computational efficiency.
Extends knockoff filter for composite null hypotheses in variable selection.
New method controls false discoveries in online testing with deadlines.
In many practical applications of multiple hypothesis testing using the False Discovery Rate (FDR), the given hypotheses can be naturally partitioned into groups, and one may not only want to control the number of false discoveries (wrongly rejected null hypotheses), but also the number of falsely discovered groups of …
A framework for hypothesis testing on attributed graphs using sampling.
DivDis learns diverse hypotheses from underspecified data to improve robustness.
The paper confirms two groups of gamma-ray bursts using a new nonparametric metric.
The paper tests hypotheses on two Lévy process-driven streams of observations.
e-LOND algorithm controls FDR in online testing with arbitrary dependencies.
Efficient algorithms identify true hypothesis from many options with minimal actions.
While statistics focusses on hypothesis testing and on estimating (properties of) the true sampling distribution, in machine learning the performance of learning algorithms on future data is the primary issue. In this paper we bridge the gap with a general principle (PHI) that identifies hypotheses with best predictive…
New method quantifies reliability of neural network image segmentation.
The hypothesis of randomness is fundamental in statistical machine learning and in many areas of nonparametric statistics; it says that the observations are assumed to be independent and coming from the same unknown probability distribution. This hypothesis is close, in certain respects, to the hypothesis of exchangeab…
Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. This work answers a basic question about privately testing simple hypotheses: given two distributions and , and a privacy level , how many i.i.d. samples are needed to…
New algorithms improve FDR control in sequential hypothesis testing.
Hypothesis testing in the linear regression model is a fundamental statistical problem. We consider linear regression in the high-dimensional regime where the number of parameters exceeds the number of samples (). In order to make informative inference, we assume that the model is approximately sparse, that is th…
We propose procedures for testing whether stock price processes are martingales based on limit order type betting strategies. We first show that the null hypothesis of martingale property of a stock price process can be tested based on the capital process of a betting strategy. In particular with high frequency Markov …
The paper develops online methods to control familywise error rate in growing hypothesis testing sequences.
Machine Learning benefits from prior information and computational power for better performance and understanding.
Detects dense subhypergraphs in heterogeneous random hypergraphs.
DP-SPRT improves privacy in sequential tests with near-optimal error rates.
The properties of statistical tests for hypotheses concerning the parameters of the multifractal model of asset returns (MMAR) are investigated, using Monte Carlo techniques. We show that, in the presence of multifractality, conventional tests of long memory tend to over-reject the null hypothesis of no long memory. Ou…
Finding statistically significant interactions between binary variables is computationally and statistically challenging in high-dimensional settings, due to the combinatorial explosion in the number of hypotheses. Terada et al. recently showed how to elegantly address this multiple testing problem by excluding non-tes…
Context. Generative models open up the possibility to interrogate scientific data in a more data-driven way. Aims: We propose a method that uses generative models to explore hypotheses in astrophysics and other areas. We use a neural network to show how we can independently manipulate physical attributes by encoding ob…
Robust hypothesis testing designs a test for worst-case distributions using kernel methods.
A new framework selects information sources to test hypotheses robustly, even with misclassifications.
This article develops a framework for testing general hypothesis in high-dimensional models where the number of variables may far exceed the number of observations. Existing literature has considered less than a handful of hypotheses, such as testing individual coordinates of the model parameter. However, the problem o…
More powerful feature selection tests using selective inference.
Crowdsourcing has been successfully applied in many domains including astronomy, cryptography and biology. In order to test its potential for useful application in a Smart Grid context, this paper investigates the extent to which a crowd can contribute predictive hypotheses to a model of residential electric energy con…
Multiple hypothesis testing is a core problem in statistical inference and arises in almost every scientific field. Given a set of null hypotheses , Benjamini and Hochberg introduced the false discovery rate (FDR), which is the expected proportion of false positives among rejected nu…
LLM agents discover cryptocurrency factors under reproducible constraints.
Unified method for MMD variance estimation improves accuracy and computational efficiency.
This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models. Motivated by the geometric projection principle, we propose new decorrelated score, Wald and partial likelihood ratio statistics. Wi…
Adaptive auditing improves AI robustness testing with anytime-valid guarantees.
The statistical analysis of discrete data has been the subject of extensive statistical research dating back to the work of Pearson. In this survey we review some recently developed methods for testing hypotheses about high-dimensional multinomials. Traditional tests like the test and the likelihood ratio test ca…
Hypothesis testing is an important cognitive process that supports human reasoning. In this paper, we introduce a computational hypothesis testing approach based on memory augmented neural networks. Our approach involves a hypothesis testing loop that reconsiders and progressively refines a previously formed hypothesis…