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…

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…

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

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

This paper illustrates a procedure for fitting financial data with $α$-stable distributions. After using all the available methods to evaluate the distribution parameters, one can qualitatively select the best estimate and run some goodness-of-fit tests on this estimate, in order to quantitatively assess its quality. I…

New spectral tests assess network model fits efficiently.

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

Two new tests assess how well conditional models fit data.

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

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

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

The $κ$-generalised distribution fits daily stock returns well.

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…

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

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

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

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…

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.

Software package assesses spherical data distributions and clusters.

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

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

CEDA improves understanding of data fit to models.

RENAL test evaluates generative models for time series data.

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…

Improved KSD test for better detection of differences in distributions.

Fourier representation improves KSD for infinite-dimensional data.

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

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

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…

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

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.

Two models are identified for robust cross-impact analysis.

The paper fits a seven-parameter GTS distribution to financial data.

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

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…

We develop a general theory for the goodness-of-fit test to non-linear models. In particular, we assume that the observations are noisy samples of a submanifold defined by a \yao{sufficiently smooth non-linear map}. The observation noise is additive Gaussian. Our main result shows that the "residual" of the model fit, …

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

In the spirit of the emergent field of econophysics, a goodness-of-fit test for the Power-Law distribution, based on the Empirical Distribution Function (EDF) is presented, and related problems are discussed. An analysis of the tail behaviour of the daily logarithmic variation of the Mexican Stock Market Index (IPC), s…

New test assesses probabilistic model calibration without expensive approximations.