arXiv research

New bootstraps improve speed and accuracy for graph count functionals.

Paper develops methods for statistical inference with SGD in nonconvex optimization.

The study identifies extremal dependence in financial markets using a bootstrap-based testing procedure.

The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets---which are increasingly prevalent---the computation of bootstrap-based quantities can be prohibitively demanding computationally. While variants such as subsampling and the $m$ out o…

New insights into bootstrapping for bandits, improving efficiency and performance.

The intention of this paper is to estimate a Bayesian distribution-free chain ladder (DFCL) model using approximate Bayesian computation (ABC) methodology. We demonstrate how to estimate quantities of interest in claims reserving and compare the estimates to those obtained from classical and credibility approaches. In …

Paper proposes new method for time series confidence intervals using LSTM.

Proposes a private empirical bootstrap for Gaussian Differential Privacy.

New DP bootstrap method for statistical inference with improved privacy and accuracy.

Develops a novel fast bootstrap for dependent data with higher-order accuracy.

The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets, the computation of bootstrap-based quantities can be prohibitively demanding. As an alternative, we present the Bag of Little Bootstraps (BLB), a new procedure which incorporates fea…

Paper develops Gaussian approximations and bootstrap for federated LSA with trade-off bounds.

Paper uses HGLM and bootstrap for loss reserve error estimation in insurance.

This paper tackles noisy multi-objective optimization with adaptive resampling using bootstrapping.

Bootstrap methods fail in high-dimensional linear regression.

Paper improves bootstrapping for off-policy reinforcement learning inference.

Develops statistical confidence sets for multidimensional scaling.

Paper improves confidence intervals for LSA with multiplier bootstrap.

Paper improves confidence set construction for SGD using multiplier bootstrap.

In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a single model that gives efficient statistical estimation. A simple method is to linearly average the parameters of the local models, which, howev…

Structural equation models and Bayesian networks have been widely used to study causal relationships between continuous variables. Recently, a non-Gaussian method called LiNGAM was proposed to discover such causal models and has been extended in various directions. An important problem with LiNGAM is that the results a…

Gaussian and bootstrap methods improve ATE estimator accuracy.

SIREN protocol corrects optimistic winner's scores in LLM evaluation.

We introduce a bootstrap procedure for high-frequency statistics of Brownian semistationary processes. More specifically, we focus on a hypothesis test on the roughness of sample paths of Brownian semistationary processes, which uses an estimator based on a ratio of realized power variations. Our new resampling method,…

A new non parametric approach to the problem of testing the independence of two random process is developed. The test statistic is the Hilbert Schmidt Independence Criterion (HSIC), which was used previously in testing independence for i.i.d pairs of variables. The asymptotic behaviour of HSIC is established when compu…

We consider the least-square linear regression problem with regularization by the $\ell^1$-norm, a problem usually referred to as the Lasso. In this paper, we first present a detailed asymptotic analysis of model consistency of the Lasso in low-dimensional settings. For various decays of the regularization parameter, w…

This paper develops bootstrap methods to assess uncertainty in variational inference.

GANs generate samples from time series data.

In this paper, we detail the main simulation methods used in practice to measure one-year reserve risk, and describe the bootstrap method providing an empirical distribution of the Claims Development Result (CDR) whose variance is identical to the closed-form expression of the prediction error proposed by Wüthrich et a…

A novel deep bootstrap framework for nonparametric regression using conditional diffusion models.

FAB combines flows with AIS to approximate complex distributions.

We analyze and correct bias in template shape estimation using geometric statistics.

We develop a flexible framework for low-rank matrix estimation that allows us to transform noise models into regularization schemes via a simple bootstrap algorithm. Effectively, our procedure seeks an autoencoding basis for the observed matrix that is stable with respect to the specified noise model; we call the resul…

Probabilistic graphical models are graphical representations of probability distributions. Graphical models have applications in many fields including biology, social sciences, linguistic, neuroscience. In this paper, we propose directed acyclic graphs (DAGs) learning via bootstrap aggregating. The proposed procedure i…

New test detects differences in heterogeneous datasets.

New method aggregates multilayer networks for financial and other applications.

New method for estimating and testing impulse responses in high-dimensional VAR systems.

Paper improves CLT and bootstrap approximations for LSA with decreasing step size.

Method cleans covariance matrices for better statistical inference.

The paper develops methods for causal function estimation and inference with multiway clustered data.

Paper revisits pre-validation method, improving hypothesis testing.

Paper introduces a new test for conditional independence using weighted partial copulas.

The paper provides Gaussian approximations for decentralized Federated Learning.

We present a general derivation of the arbitrage-free pricing framework for multiple-currency collateralized products. We include the impact on option pricing of the policy adopted to fund in foreign currency, so that we are able to price contracts with cash flows and/or collateral accounts expressed in foreign currenc…

Twin-Boot integrates uncertainty estimation into optimization using parallel training of identical models.

Proposes two-stage robust and sparse distributed inference for large-scale data.

New method for uncertainty quantification in private SGD.

A novel bootstrap method improves concept drift detection in predictive models.