arXiv research

Fast nonparametric conditional independence testing via two-stage regression

Computer vision model automates residual plot assessment for diagnosing model assumptions.

Paper introduces a new risk measure for multivariate residual estimation.

DIET tests conditional independence using marginal dependence measures of residual information.

Study excess logarithmic residues for foliations to bound invariant hypersurfaces and test log canonicity.

The paper extends SARMA models by relaxing independence assumptions on error terms.

In this paper, we build tests for the presence of residual noise in a model where the market microstructure noise is a known parametric function of some variables from the limit order book. The tests compare two distinct quasi-maximum likelihood estimators of volatility, where the related model includes a residual nois…

The paper introduces a diagnostic method to detect grokking transitions in models before test accuracy improves.

New diagnostic method detects misspecified models in inverse PDE problems.

GaussDetect-LiNGAM eliminates Gaussianity tests for causal discovery.

Deep residual networks trained with gradient descent have small generalization gap.

Deviance Voronoi residuals improve earthquake insurance risk assessment.

Study tests how U.S. equity prices align with global asset frequencies using financial variables.

ResMem improves model generalization by explicitly memorizing residuals.

High-frequency data cointegration framework developed with rigorous theory and tests.

Develops efficient inference for noise heterogeneity in machine learning models.

It is now widely accepted that, to model the dynamics of daily financial returns, volatility models have to incorporate the so-called leverage effect. We derive the asymptotic behaviour of the squared residuals autocovariances for the class of asymmetric power GARCH model when the power is unknown and is jointly estima…

PRISMA uses PDE residuals for fast, robust, and accurate inference.

Cross validation residuals are well known for the ordinary least squares model. Here leave-M-out cross validation is extended to generalised least squares. The relationship between cross validation residuals and Cook's distance is demonstrated, in terms of an approximation to the difference in the generalised residual …

Alternative to likelihood-based LSNM model selection, residual independence testing is more robust to noise misspecification.

Deep learning for HJB PDEs using synthetic data and residual minimization.

New method for accurate permutation inference in CCA.

New method detects strong calibration in ML models, even for small poorly calibrated subgroups.

Study provides selective inference method for latent block models.

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polyso…

Novel LSTM network predicts pulsar timing residuals with few-shot data.

We present a self-consistent model for explosive financial bubbles, which combines a mean-reverting volatility process and a stochastic conditional return which reflects nonlinear positive feedbacks and continuous updates of the investors' beliefs and sentiments. The conditional expected returns exhibit faster-than-exp…

We present in this paper a model for forecasting short-term power loads based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated…

Study of $η$ invariants on lens spaces detects distinctions invisible to ordinary $η$.

Develops goodness-of-fit tests for noisy submanifold samples.

STRIC detects anomalies in time series by analyzing residual signals.

Residual generation helps diagnose engine faults using neural networks.

A new method improves maximum inner product search by locally decomposing residual vectors.

Effective Gram matrix predicts deep network generalization.

NOTEARS fails to identify true causal relationships from data.

Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.

Deep learning outperforms Black-Scholes in Brazilian Petrobras option pricing.

Financial econometrics has become an increasingly popular research field. In this paper we review a few parametric and nonparametric models and methods used in this area. After introducing several widely used continuous-time and discrete-time models, we study in detail dependence structures of discrete samples, includi…

In a flat space, the global topology of comoving space can induce a weak acceleration effect similar to dark energy. Does a similar effect occur in the case of the Poincare dodecahedral space S^3/I^*? Does the effect distinguish the Poincare space from other well-proportioned spaces? The residual acceleration effect in…

Analytical tools for pricing power options in Lévy models.

Proposes a modified Morgan-Pitman test for evaluating variances in machine learning models.

Replicates deep learning strategy for trading factor residuals, finds strong performance.

Develops a semi-parametric model for dynamic pricing in e-commerce.

We speed up Gaussian process cross-validation calculations and improve model diagnostics.

MLP residual networks implement a selective coarse-graining procedure governed by the spectral structure of the input distribution.

New method for learning causal relationships in PNL models.

Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks. The explanations in the literature for the apparent effectiveness of shortcuts are varied and often contradictory. We hypothesize that shortcut…

Paper proposes graph-based separable transforms for video coding.