New robustness test for kernel goodness-of-fit tests.
problem Lack of robustness in existing kernel goodness-of-fit tests.
method Proposes a new robust kernel goodness-of-fit test using kernel Stein discrepancy (KSD) balls.
result First robust kernel goodness-of-fit test addressing both qualitative and quantitative robustness.
A new metric uses nonparametric comparison for fitting parametric distributions.
problem Measuring goodness-of-fit for nonlinear models using maximum likelihood estimation.
method Survival Jensen-Shannon divergence (SJS) and its empirical counterpart (ESJS) for nonparametric comparison. result The ESJS can be used as a measure of goodness-of-fit in maximum likelihood estimation. A new kernel Stein test assesses fit for variable-length sequential data.
problem Evaluating goodness of fit for varying-dimensional data like text documents of different lengths.
method Extends kernel Stein discrepancy (KSD) to variable-dimension settings by identifying appropriate Stein operators and proposing a novel KSD goodness-of-fit test.
result The proposed test performs well on discrete sequential data benchmarks.
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…
New tests measure model goodness of fit with interpretable features.
problem Measuring relative goodness of fit between two models.
method Nonparametric, computationally efficient tests producing informative features.
result Test power matches state-of-the-art but is one order faster.
Neyman-Pearson testing improves goodness of fit in detecting new physics.
problem Detecting small anomalies in data distributions.
method Employing Neyman-Pearson strategy with a rich parametrized family of models.
result Neyman-Pearson testing is more sensitive to small departures and unbiased towards specific anomalies.
Improved SSD for faster and more accurate goodness-of-fit tests and model learning.
problem Optimal slicing directions for SSD are computationally expensive and sub-optimal.
method Relaxed optimal slicing requirement, active sub-space construction, spectral decomposition.
result 14-80x speed-up in goodness-of-fit tests compared to gradient-based alternatives.
New feature Stein discrepancies improve sampler selection and goodness-of-fit testing.
problem Computational inefficiency in existing Stein discrepancies.
method Feature Stein Discrepancies (ΦSDs) and Random Feature Stein Discrepancies (RΦSDs).
result RΦSDs are orders of magnitude faster while maintaining or improving performance.
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.
problem Determining if network models fit data well and extrapolate.
method Random matrix theory-derived goodness-of-fit tests.
result General approach simplifies parameter selection in network models.
Linear-time kernel test outperforms previous methods in goodness-of-fit.
problem Testing goodness-of-fit for large datasets efficiently.
method Adaptive feature learning via Stein's method to minimize false negatives.
result The new test outperforms previous methods under mean-shift alternatives and high dimensions.
Develops goodness-of-fit tests for noisy submanifold samples.
problem Testing non-linear models on noisy submanifold samples.
method Non-linear least-square problem solution and χ2 distribution application. result Residual follows χ2 distribution with parameters related to model order and dimension. Sliced kernelized Stein discrepancy improves goodness-of-fit tests and model learning in high dimensions.
problem The curse-of-dimensionality in kernelized Stein discrepancy (KSD).
method Sliced Stein discrepancy and its scalable variants using optimal one-dimensional projections.
result Significantly outperforms KSD and baselines in goodness-of-fit tests and improves model learning.
Two new tests assess how well conditional models fit data.
problem Assessing goodness of fit for conditional distributions.
method Nonparametric statistical tests using Stein operators.
result Tests are consistent and interpretable.
Paper develops a minimax optimal test for goodness-of-fit using kernel Stein discrepancy.
problem Developing a robust goodness-of-fit test for general domains.
method Kernel Stein Discrepancy (KSD) with spectral regularization and adaptive testing.
result Proposed regularized test achieves minimax optimality up to a logarithmic factor.
GRASP tests goodness-of-fit for binary classifiers without parametric assumptions.
problem Assessing the fit of a binary classifier to the underlying conditional law of labels given features.
method Formulates a tolerance hypothesis testing problem and proposes a novel test called GRASP.
result Proposes GRASP and Model-X GRASP tests for assessing goodness-of-fit in finite sample settings.
Develops a goodness-of-fit test for self-exciting processes.
problem Quantifying how well generative models capture self-exciting point processes.
method Connects to Quasi-maximum-likelihood estimator (QMLE) theory and develops a non-parametric self-normalizing statistic, the Generalized Score (GS) statistics.
result Validates the proposed GS test's good performance through numerical simulation and real-data experiments.
The κ-generalised distribution fits daily stock returns well.
problem Stock returns are often heavy-tailed, not normally distributed.
method Used the κ-generalised distribution with a Monte-Carlo goodness of fit test. result The κ-generalised distribution fits historic daily stock returns well for a significant proportion of analyzed stocks. 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.
problem Testing the goodness of fit for ERGMs with a single network observation.
method Kernel Stein discrepancy combined with a discrete Stein operator for ERGMs, Monte Carlo simulation.
result The test provides theoretical and practical support for assessing ERGM fit.
KSDAgg combines multiple KSD tests to improve goodness-of-fit testing without splitting data.
problem Improving goodness-of-fit testing without data splitting.
method KSDAgg aggregates multiple KSD tests with different kernels to maximize power.
result KSDAgg achieves the smallest uniform separation rate of the collection, up to a logarithmic term.
Study uses copulas and DCC-GARCH for multivariate risk analysis of VaR and CVaR.
problem Multivariate risk analysis for Value at Risk (VaR) and Conditional Value at Risk (CoVaR).
method Copulas and Dynamic Conditional Correlation (DCC)-GARCH models applied to historical financial data.
result Comparison of different copula families for goodness-of-fit and effectiveness.
Optimal tests for goodness of fit and two-sample problems using MMD and KSD.
problem Asymptotically optimal tests for goodness of fit and two-sample problems.
method Maximum Mean Discrepancy (MMD) and Kernel Stein Discrepancy (KSD) based tests.
result Optimal tests achieve the maximum exponential decay rate under specific conditions.
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 test compares data to ergodic Markov models without specifying an alternative.
problem Testing goodness of fit for ergodic Markov processes without an alternative model.
method Density-based test comparing data to specified models' stationary densities.
result Test provides new insights into econometric and financial modeling.
Unified neural network model for astro-particle physics predictions with coverage, systematics, and goodness-of-fit.
problem Lack of statistical uncertainties, coverage, systematic uncertainties, and goodness-of-fit in neural network predictions.
method KL-divergence objective for joint distribution of data and labels, conditional normalizing flows, amortized with neural networks.
result Unified supervised learning and VAEs under stochastic variational inference for event property predictions.
New autoencoder uses goodness-of-fit tests for better model performance.
problem Improving the goodness-of-fit in generative models.
method Develops Goodness-of-Fit Autoencoder (GoFAE) incorporating GoF tests at minibatch and global levels.
result GoFAE achieves comparable performance to deep generative models while retaining statistical indistinguishability.
Proposes a new Bayesian modeling framework.
problem Establishing principled priors and consolidating Bayesian analysis.
method Bayes via goodness of fit.
result Shows practical benefits of new approach.
Software package assesses spherical data distributions and clusters.
problem Assessing and clustering spherical data distributions.
method Innovative goodness-of-fit tests and clustering algorithms using kernel-based quadratic distances.
result Efficient and mathematically sound goodness-of-fit tests for spherical data.
A new framework improves kernel Stein discrepancy tests for validating distributions.
problem Improving goodness-of-fit testing for non-normal distributions.
method Introducing Sf-KSD, a unifying framework for studying Stein operators in KSD-based tests.
result Sf-KSD guides the development of new tests and outperforms existing methods.
New method tests fit between source and target populations.
problem Testing goodness-of-fit under covariate shift.
method Truncated importance-weighting kernel ridge regression with multiplier bootstrap.
result Valid and sharp confidence sets for regression function.
New deep learning methods improve estimation and GOF assessment for large-scale IFA.
problem Estimating and assessing goodness-of-fit for large-scale confirmatory IFA models.
method Extended deep learning algorithm for parameter estimation and simulation-based tests for GOF assessment.
result Proposed methods provide comparable estimates and detect latent dimensionality misspecification.
CEDA improves understanding of data fit to models.
problem Real-world data often deviates from theoretical models.
method Categorical Exploratory Data Analysis (CEDA) to highlight deviations.
result CEDA reveals where and how data fits or deviates from models.
RENAL test evaluates generative models for time series data.
problem Evaluating generative models for time series data is challenging.
method RENAL test uses recurrent neural networks to transform time series into conditionally independent data pairs for goodness-of-fit testing.
result RENAL test outperforms existing methods in evaluating generative models for time series data.
Improved KSD test for better detection of differences in distributions.
problem Low power of KSD test when distributions have same modes but different mixing proportions.
method Perturb the observed sample using Markov transition kernels to improve KSD test power.
result Perturbed KSD test can lead to substantially higher power than the original KSD test.
Hawkes processes show weak causality; likelihoods are nearly equal for forward and backward event times.
problem Testing the causality of Hawkes processes with time reversal.
method Maximum likelihood estimation and goodness-of-fit tests.
result Parameter estimation of Hawkes processes is weakly dependent on the direction of time, and significant fits may favor backward time.
Fourier representation improves KSD for infinite-dimensional data.
problem Applying KSD to infinite-dimensional data.
method Combining measure equations with kernel methods for a Fourier representation of KSD.
result KSD can separate measures in infinite-dimensional Hilbert spaces.
Kernel-embedding tests can be suboptimal, but a simple modification improves their performance.
problem Optimizing goodness-of-fit tests using kernel embeddings.
method Analyzing and modifying kernel-embedding based goodness-of-fit tests within a minimax framework.
result A moderated kernel-embedding approach provides optimal tests for various deviations and is adaptive over a wide range of spaces.
The paper introduces tests for missing data models based on graph assumptions.
problem Verification of assumptions in missing data models is insufficiently addressed.
method The paper explores three classes of missing data models and designs goodness-of-fit tests.
result The paper provides new insights and tests for missing data graphical models.
New test for latent block models to determine cluster numbers.
problem No statistical test for latent block models.
method Developed a goodness-of-fit test using random matrix theory.
result Demonstrated the effectiveness of the test method.
New method uses reinforcement learning to sample from complex data structures efficiently.
problem Constructing reliable samples from high-dimensional polytopes for goodness-of-fit tests.
method Markov decision process and reinforcement learning for sampling.
result Demonstrated scalable tools from linear algebra for theoretical guarantees in non-linear algebra context.
Two diagnostics improve variational inference quality.
problem Difficulties in evaluating variational approximations.
method PSIS diagnostic for joint distributions and VSBC for point estimates.
result Improves accuracy and reliability of variational approximations.
Improved MMD test for non-Euclidean data with spectral regularization.
problem Inefficient and impractical MMD goodness-of-fit tests for non-Euclidean data.
method Spectral regularization of MMD test, extending results to general cases.
result Minimax optimal test for non-Euclidean data with appropriate 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.
problem Inaccurate fitting of classical distributions to insurance claim frequency data.
method Modeling claim frequencies using random proportions of insurance contracts and applying goodness-of-fit tests.
result A new statistical approach for better modeling insurance claim frequencies.
A new sequential test for unnormalized densities.
problem Testing unnormalized densities with adaptive stopping.
method Sequential kernelized Stein discrepancy test, using non-uniform Stein kernels.
result Valid test with asymptotic lower bound for growth.
Develops a universal test for assessing dynamic network models.
problem Determine if observed networks match a candidate dynamic random graph model.
method Formulates and analyzes a universal test for graph-valued, infinite-state Markov processes.
result Exhibits and analyzes a universal test for a natural class of models.
Two models are identified for robust cross-impact analysis.
problem Developing and validating cross-impact models that fit data and are well-behaved.
method Classified cross-impact models according to desirable properties and evaluated them on three asset classes.
result Only one model satisfies all desirable properties and is suitable for applications.