arXiv research

Paper compares LSTM and GARCH for estimating value-at-risk.

This paper develops a Carleman type estimate for immersed surface in Euclidean space at infinity. With this estimate, we obtain an unique continuation property for harmonic functions on immersed surfaces vanishing at infinity, which leads to rigidity results in geometry.

We study how to efficiently estimate average treatment effects (ATEs) using adaptive experiments. In adaptive experiments, experimenters sequentially assign treatments to experimental units while updating treatment assignment probabilities based on past data. We start by defining the efficient treatment-assignment prob…

Unified theory for causal inference using various methods.

Gaussian and bootstrap methods improve ATE estimator accuracy.

Proposes RCL method to improve ATE estimation from observational data.

This study optimizes neural networks for doubly robust ATE estimation to balance bias and variance.

This study optimizes covariate density and propensity score for efficient ATE estimation.

Study proposes a new method to estimate bias-correction term for ATE estimation.

FIDDLE uses deep learning to estimate ATE from complex data.

New method estimates treatment effects from treated and unlabeled units.

We develop some estimates under the Ricci flow and use these estimates to study the blowup rates of curvatures at singularities. As applications, we obtain some gap theorems: $\displaystyle \sup_X |Ric|$ and $\displaystyle \sqrt{\sup_X |Rm|} \cdot \sqrt{\sup_X |R|}$ must blowup at least at the rate of type-I. Our estim…

DOPE efficiently estimates ATE with complex covariates.

The consistency of doubly robust estimators relies on consistent estimation of at least one of two nuisance regression parameters. In moderate to large dimensions, the use of flexible data-adaptive regression estimators may aid in achieving this consistency. However, $n^{1/2}$-consistency of doubly robust estimators is…

Study uses surrogate data to improve treatment effect estimation with scarce outcome data.

We estimate risk measures in Markov cost processes with lower and upper bounds.

A-TMLE estimates ATE from RCT and RWD, achieving super-efficiency.

This research improves value-at-risk estimation during financial crises using non-extensive statistical methods.

New approach estimates treatment effects from decentralized data.

New estimator for survival function with missing not at random censoring indicators.

Paper presents efficient IS for tail risk estimation with machine learning features.

Paper introduces ps-BART for estimating nonlinear ATE and CATE in continuous treatments.

Improved quantile estimation model for VaR.

GARCH-UGH improves VaR estimation for financial risk management.

Ricean channel model is widely used in wireless communications to characterize the channels with a line-of-sight path. The Ricean K factor, defined as the ratio of direct path and scattered paths, provides a good indication of the link quality. Most existing works estimate K factor based on either maximum-likelihood cr…

Paper proposes real-time VaR estimation using quantile regression forest with conformal calibration.

Estimates MLP expected output without sampling, using fewer FLOPs.

Algorithm reduces variance in causal effect estimation from multiple datasets.

Bayesian method for estimating ATE with robustness to model misspecification.

The paper uses deep neural networks to estimate and infer ATE without needing to know the dimension of the data.

Simple private estimators for mean and covariance outperform existing methods.

We prove an interior Schauder estimate for the Laplacian on metric products of two dimensional cones with a Euclidean factor, generalizing the work of Donaldson and reproving the Schauder estimate of Guo-Song. We characterize the space of homogeneous subquadratic harmonic functions on products of cones, and identify sc…

In this paper, we are interested in the strong convergence properties of the Ninomiya-Victoir scheme which is known to exhibit weak convergence with order 2. We prove strong convergence with order $1/2$. This study is aimed at analysing the use of this scheme either at each level or only at the finest level of a multil…

We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by applications where response varies by individuals and therefore, estimating resp…

Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent work, MINE (Belghazi et al. 2018), focused on estimating tight variational lower bounds of MI using neural networks, but assumed unlimited supply of samp…

Sharp bounds on ATE with unmeasured confounders, valid even when misspecified.

Paper proposes ClipSMT algorithm for better ATE estimation.

New method estimates minimizer and minimum value of a regression function.

The paper establishes curvature estimates for solitons in higher dimensions.

Study on predicting graph labels at nodes using local averaging and distance estimation.

Extended univariate Range Value-at-Risk to multivariate settings.

For purposes of Value-at-Risk estimation, we consider several multivariate families of heavy-tailed distributions, which can be seen as multidimensional versions of Paretian stable and Student's t distributions allowing different marginals to have different tail thickness. After a discussion of relevant estimation and …

In a recent paper, Brendle proved that the inscribed radius of closed embedded mean convex hypersurfaces moving by mean curvature flow is at least 1/((1+δ)H) at all points with H > C(δ,M_0). In this note, we give a shorter proof of Brendle's estimate, and of a more general result for alpha-Andrews flows, based on our r…

Private estimation of many quantiles using differential privacy.

Study shows how weak inverse anisotropic mean curvature flow behaves at infinity.

Quantum SVT reduces credit risk analysis costs.

Paper improves VaR risk allocation by avoiding zero probability events.

New estimates for Hitchin's equations at high energy.