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.
Cheap permutation tests speed up distribution testing without sacrificing accuracy.
A method uses neural networks to approximate sampling distributions of test statistics.
Estimates peeking effects in p-values to correct bias.
Efficient tests for various statistical problems using incomplete U-statistics.
Simple methods combine statistical tests for out-of-distribution detection.
aLTT selects hyperparameters efficiently with statistical guarantees.
Enhances power of covariance matrix tests for high-dimensional data.
Testing independence is of significant interest in many important areas of large-scale inference. Using extreme-value form statistics to test against sparse alternatives and using quadratic form statistics to test against dense alternatives are two important testing procedures for high-dimensional independence. However…
Hypothesis tests are a crucial statistical tool for data mining and are the workhorse of scientific research in many fields. Here we present a differentially private analogue of the classic Wilcoxon signed-rank hypothesis test, which is used when comparing sets of paired (e.g., before-and-after) data values. We present…
Efficient tests achieve best error rates in high-dimensional hypothesis testing.
Paper proposes a statistical test for feature selection pipelines using selective inference.
Framework tests group fairness in machine learning models.
Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is used to detect dependence between a categorical and a numerical variable. Here we show how one can carry out this hypothesis test under the…
We propose a nonparametric statistical test for goodness-of-fit: given a set of samples, the test determines how likely it is that these were generated from a target density function. The measure of goodness-of-fit is a divergence constructed via Stein's method using functions from a Reproducing Kernel Hilbert Space. O…
We have developed a statistical technique to test the model assumption of binary regime switching extension of the geometric Brownian motion (GBM) model by proposing a new discriminating statistics. Given a time series data, we have identified an admissible class of the regime switching candidate models for the statist…
Statistical query algorithms and low-degree tests are nearly equivalent in high-dimensional hypothesis testing.
Statistical test verifies long-term rating system calibration with overlapping time windows.
Statistical tests for fairness in admissions data reveal hidden patterns.
USP test improves on Pearson's chi-squared and -test for independence.
The article explains how to estimate confusion matrices for classifiers using unlabeled data.
E-C2ST uses E-values for high-dimensional data two-sample tests.
Testing two potentially multivariate variables for statistical dependence on the basis finite samples is a fundamental statistical challenge. Here we explore a family of tests that adapt to the complexity of the relationship between the variables, promising robust power across scenarios. Building on the distance correl…
We develop a pivotal test to assess the statistical significance of the feature variables in a single-layer feedforward neural network regression model. We propose a gradient-based test statistic and study its asymptotics using nonparametric techniques. Under technical conditions, the limiting distribution is given by …
This work improves independence tests for high-dimensional data.
New test for conditional independence using GNNs avoids estimating conditional distributions.
Nonparametric two sample testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. We refer to the most common settings as mean difference alternatives (MDA), for testing differences only in firs…
Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence. We develop a new test based on an ensemble approach that uses the sum of squared symmetry statistics and di…
A new method uses neural tangent kernel to efficiently compute MMD statistic.
Consistently checking the statistical significance of experimental results is one of the mandatory methodological steps to address the so-called "reproducibility crisis" in deep reinforcement learning. In this tutorial paper, we explain how the number of random seeds relates to the probabilities of statistical errors. …
Statistical test evaluates if personalizing interventions is cost-effective.
Distance-based tests, also called "energy statistics", are leading methods for two-sample and independence tests from the statistics community. Kernel-based tests, developed from "kernel mean embeddings", are leading methods for two-sample and independence tests from the machine learning community. A fixed-point transf…
Study proposes a statistical testing framework for evaluating clustering pipelines.
Proposes a statistical test for VAE-based anomaly detection reliability.
New test for conditional independence using kernel embeddings.
A test for neural networks identifies genetic associations.
Deep-learning method improves hypothesis testing for independence.
There has been an increasing interest in testing the equality of large Pearson's correlation matrices. However, in many applications it is more important to test the equality of large rank-based correlation matrices since they are more robust to outliers and nonlinearity. Unlike the Pearson's case, testing the equality…
A family of maximum mean discrepancy (MMD) kernel two-sample tests is introduced. Members of the test family are called Block-tests or B-tests, since the test statistic is an average over MMDs computed on subsets of the samples. The choice of block size allows control over the tradeoff between test power and computatio…
This article develops a statistical test for the null hypothesis of strict stationarity of a discrete time stochastic process in the frequency domain. When the null hypothesis is true, the second order cumulant spectrum is zero at all the discrete Fourier frequency pairs in the principal domain. The test uses a window …
A new kernel test avoids permutations for independence testing.
We propose a two-sample testing procedure based on learned deep neural network representations. To this end, we define two test statistics that perform an asymptotic location test on data samples mapped onto a hidden layer. The tests are consistent and asymptotically control the type-1 error rate. Their test statistics…
A new test method improves goodness-of-fit tests for copulas.
Study improves statistical power for detecting algorithmic bias in educational data.
New statistics improve kernel independence testing efficiency.
This paper provides a new unimodality test with application in hierarchical clustering methods. The proposed method denoted by signature test (Sigtest), transforms the data based on its statistics. The transformed data has much smaller variation compared to the original data and can be evaluated in a simple proposed un…
Kernel two-sample testing is a useful statistical tool in determining whether data samples arise from different distributions without imposing any parametric assumptions on those distributions. However, raw data samples can expose sensitive information about individuals who participate in scientific studies, which make…
New gossip algorithms improve robustness of rank-based statistics in decentralized systems.
A new test statistic speeds up MMD while maintaining power.