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.
The paper develops approximations for Pearson's chi-square statistic and applies them to confidence intervals.
USP test improves on Pearson's chi-squared and -test for independence.
Develops an empirical likelihood framework for random forests and ensembles.
Distance correlation has gained much recent attention in the data science community: the sample statistic is straightforward to compute and asymptotically equals zero if and only if independence, making it an ideal choice to discover any type of dependency structure given sufficient sample size. One major bottleneck is…
New SQ lower bounds for NGCA without requiring chi-squared condition.
We study the information content of nuclear masses from the perspective of global models of nuclear binding energies. To this end, we employ a number of statistical methods and diagnostic tools, including Bayesian calibration, Bayesian model averaging, chi-square correlation analysis, principal component analysis, and …
Share price returns on different time scales can be well modelled by a superstatistical dynamics. Here we provide an investigation which type of superstatistics is most suitable to properly describe share price dynamics on various time scales. It is shown that while chi-square superstatistics works well on a time scale…
Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our procedure …
This paper provides performance guarantees for neural estimation of statistical distances.
The transition probability of a Cox-Ingersoll-Ross process can be represented by a non-central chi-square density. First we prove a new representation for the central chi-square density based on sums of powers of generalized Gaussian random variables. Second we prove Marsaglia's polar method extends to this distributio…
A goodness-of-fit test for DCSBM improves scalability and power for large sparse networks.
PQMass assesses generative model quality using chi-squared tests.
Four new methods for computing generalized chi-square distribution.
Study tests uniformity of categorical data against missing-ball alternatives, finding chi-squared test outperforms.
Directly simulates squared Bessel processes efficiently.
Study compares statistical properties and power of divergence measures for credit risk monitoring.
We introduce the chi-square test neural network: a single hidden layer backpropagation neural network using chi-square test theorem to redefine the cost function and the error function. The weights and thresholds are modified using standard backpropagation algorithm. The proposed approach has the advantage of making co…
Network data is prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. Yet a simple fundamental question of how to precisely quantify the statistical uncertainty associated with the identification of latent links still remain…
SBI provides more accurate pole positions than chi-squared minimization in model misspecification.
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 …
EL framework certifies and flags bias in ML models without distributional assumptions.
Tests if vertices in graphs have the same latent positions.
LMC algorithm converges to target in Chi-squared and Renyi divergence.
Logistic regression is used thousands of times a day to fit data, predict future outcomes, and assess the statistical significance of explanatory variables. When used for the purpose of statistical inference, logistic models produce p-values for the regression coefficients by using an approximation to the distribution …
A new method detects concept drift in streaming data using k-means space partitioning.
New optimization method for sampling from unknown density measures.
Study compares chi-squared divergence and KL-divergence posteriors for PAC-Bayesian bounds.
We consider the problem of comparing probability densities between two groups. A new probabilistic tensor product smoothing spline framework is developed to model the joint density of two variables. Under such a framework, the probability density comparison is equivalent to testing the presence/absence of interactions.…
Efficient ANN search for sparse embeddings in ads targeting.
This paper tests the multivariate normality of node degrees in Erdős-Rényi graphs.
RENAL test evaluates generative models for time series data.
SDYNA is a general framework designed to address large stochastic reinforcement learning problems. Unlike previous model based methods in FMDPs, it incrementally learns the structure and the parameters of a RL problem using supervised learning techniques. Then, it integrates decision-theoric planning algorithms based o…
Group Shapley evaluates feature groups in business data, improving explainability in AI.
A theory which describes the share price evolution at financial markets as a continuous-time random walk has been generalized in order to take into account the dependence of waiting times t on price returns x. A joint probability density function (pdf) which uses the concept of a Lévy stable distribution is worked out.…
A test for neural networks identifies genetic associations.
Neural networks estimate statistical divergences with performance guarantees.
Test partial effects in Frechet regression on Bures-Wasserstein manifolds.
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…
This paper investigates the utilization of maximum and average distance correlations for multivariate independence testing. We characterize their consistency properties in high-dimensional settings with respect to the number of marginally dependent dimensions, compare the advantages of each test statistic, examine thei…
In this paper, we derive a useful lower bound for the Kullback-Leibler divergence (KL-divergence) based on the Hammersley-Chapman-Robbins bound (HCRB). The HCRB states that the variance of an estimator is bounded from below by the Chi-square divergence and the expectation value of the estimator. By using the relation b…
Zigzag sampling algorithm efficiently samples from strongly log-concave distributions with low computational cost.
Transformers learn to adapt to different task difficulties and resist distribution shifts.
-divergences are a general class of divergences between probability measures which include as special cases many commonly used divergences in probability, mathematical statistics and information theory such as Kullback-Leibler divergence, chi-squared divergence, squared Hellinger distance, total variation distance e…
The scaled complex Wishart distribution is a widely used model for multilook full polarimetric SAR data whose adequacy has been attested in the literature. Classification, segmentation, and image analysis techniques which depend on this model have been devised, and many of them employ some type of dissimilarity measure…
AES scheme improves Bermudan and American option pricing for Heston models.
Improved Bayesian analysis for SVM models using a mixture sampler.
Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques …
Study robust hypothesis testing under Hellinger distance, proving lower bounds and providing tests.