arXiv research

New robustness test for kernel goodness-of-fit tests.

A new kernel Stein test assesses fit for variable-length sequential data.

In quantitative finance, we often fit a parametric semimartingale model to asset prices. To ensure our model is correct, we must then perform goodness-of-fit tests. In this paper, we give a new goodness-of-fit test for volatility-like processes, which is easily applied to a variety of semimartingale models. In each cas…

Paper develops a minimax optimal test for goodness-of-fit using kernel Stein discrepancy.

Neyman-Pearson testing improves goodness of fit in detecting new physics.

Sliced kernelized Stein discrepancy improves goodness-of-fit tests and model learning in high dimensions.

KSDAgg combines multiple KSD tests to improve goodness-of-fit testing without splitting data.

A new framework improves kernel Stein discrepancy tests for validating distributions.

Two new tests assess how well conditional models fit data.

New autoencoder uses goodness-of-fit tests for better model performance.

A new test assesses how well observed networks fit a specified ERGM model.

Improved SSD for faster and more accurate goodness-of-fit tests and model learning.

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…

GRASP tests goodness-of-fit for binary classifiers without parametric assumptions.

We propose a novel adaptive test of goodness-of-fit, with computational cost linear in the number of samples. We learn the test features that best indicate the differences between observed samples and a reference model, by minimizing the false negative rate. These features are constructed via Stein's method, meaning th…

Improved KSD test for better detection of differences in distributions.

A new sequential test for unnormalized densities.

We review the main "omnibus procedures" for goodness-of-fit testing for copulas: tests based on the empirical copula process, on probability integral transformations, on Kendall's dependence function, etc, and some corresponding reductions of dimension techniques. The problems of finding asymptotic distribution-free te…

Develops a goodness-of-fit test for self-exciting processes.

New spectral tests assess network model fits efficiently.

Improved MMD test for non-Euclidean data with spectral regularization.

We derive a new discrepancy statistic for measuring differences between two probability distributions based on combining Stein's identity with the reproducing kernel Hilbert space theory. We apply our result to test how well a probabilistic model fits a set of observations, and derive a new class of powerful goodness-o…

RENAL test evaluates generative models for time series data.

A new test method improves goodness-of-fit tests for copulas.

The paper introduces tests for missing data models based on graph assumptions.

New test assesses probabilistic model calibration without expensive approximations.

Given two candidate models, and a set of target observations, we address the problem of measuring the relative goodness of fit of the two models. We propose two new statistical tests which are nonparametric, computationally efficient (runtime complexity is linear in the sample size), and interpretable. As a unique adva…

Computable Stein discrepancies have been deployed for a variety of applications, ranging from sampler selection in posterior inference to approximate Bayesian inference to goodness-of-fit testing. Existing convergence-determining Stein discrepancies admit strong theoretical guarantees but suffer from a computational co…

A new test for IRG models using KSD for small networks.

Fourier representation improves KSD for infinite-dimensional data.

Paper proposes kernel-based tests for model misspecification.

Software package assesses spherical data distributions and clusters.

The reproducing kernel Hilbert space (RKHS) embedding of distributions offers a general and flexible framework for testing problems in arbitrary domains and has attracted considerable amount of attention in recent years. To gain insights into their operating characteristics, we study here the statistical performance of…

Unified score and distance-based GoF tests for model adequacy.

We characterize the asymptotic performance of nonparametric goodness of fit testing. The exponential decay rate of the type-II error probability is used as the asymptotic performance metric, and a test is optimal if it achieves the maximum rate subject to a constant level constraint on the type-I error probability. We …

Proposes a semi-Bayesian nonparametric estimator for MMD in GOF tests and GANs.

New deep learning methods improve estimation and GOF assessment for large-scale IFA.

A goodness-of-fit test for DCSBM improves scalability and power for large sparse networks.

Paper proposes kernelized Stein tests for time-to-event data with censoring.

Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing

Latent block models are used for probabilistic biclustering, which is shown to be an effective method for analyzing various relational data sets. However, there has been no statistical test method for determining the row and column cluster numbers of latent block models. Recent studies have constructed statistical-test…

New method uses reinforcement learning to sample from complex data structures efficiently.

We revisit the Kolmogorov-Smirnov and Cramér-von Mises goodness-of-fit (GoF) tests and propose a generalisation to identically distributed, but dependent univariate random variables. We show that the dependence leads to a reduction of the "effective" number of independent observations. The generalised GoF tests are not…

CSD improves goodness-of-fit testing for higher-order dependence.

Optimal testing for densities under local differential privacy constraints.

A new method validates generative models in high-dimensional data.

The paper advances U-statistics in dependent settings, improving spectral estimation and goodness-of-fit tests.

Improved kernel Stein discrepancy for large-scale data.