arXiv research

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

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

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

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…

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

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

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

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

New spectral tests assess network model fits efficiently.

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

Unified neural network model for astro-particle physics predictions with coverage, systematics, and goodness-of-fit.

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

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

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

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…

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

The coefficient of determination, known as $R^2$, is commonly used as a goodness-of-fit criterion for fitting linear models. $R^2$ is somewhat controversial when fitting nonlinear models, although it may be generalised on a case-by-case basis to deal with specific models such as the logistic model. Assume we are fittin…

RENAL test evaluates generative models for time series data.

Software package assesses spherical data distributions and clusters.

Improved KSD test for better detection of differences in distributions.

Study uses copulas and DCC-GARCH for multivariate risk analysis of VaR and CVaR.

Fourier representation improves KSD for infinite-dimensional data.

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

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…

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

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…

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…

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…

A new sequential test for unnormalized densities.

We propose two nonparametric statistical tests of goodness of fit for conditional distributions: given a conditional probability density function $p(y|x)$ and a joint sample, decide whether the sample is drawn from $p(y|x)r_x(x)$ for some density $r_x$. Our tests, formulated with a Stein operator, can be applied to any…

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

Improved kernel Stein discrepancy for large-scale data.

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

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

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…

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

The two key issues of modern Bayesian statistics are: (i) establishing principled approach for distilling statistical prior that is consistent with the given data from an initial believable scientific prior; and (ii) development of a Bayes-frequentist consolidated data analysis workflow that is more effective than eith…

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

New test assesses probabilistic model calibration without expensive approximations.

A new method for modeling insurance claim frequencies using random proportions.

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

Expert-guided model improves seismic compliance monitoring.

CEDA improves understanding of data fit to models.

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…

Paper proposes kernel-based tests for model misspecification.

Two models are identified for robust cross-impact analysis.

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

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…