arXiv research

Optimal Strichartz estimates for Schrödinger on Zoll manifolds.

Improved Strichartz estimates for Schrödinger equation on negatively curved manifolds.

We demonstrate a family of Strichartz estimates for the conformally invariant Klein-Gordon equation on a class of asymptotically de Sitter spaces with C^2 metrics by using well-known local Strichartz estimates and a rescaling argument. This class of metrics includes de Sitter space. We also give an application of the e…

The study proves Strichartz and spectral projection theorems on specific types of curved surfaces.

We prove local in time Strichartz estimates without loss for the restriction of the solution of the Schroedinger equation, outside a large compact set, on a class of asymptotically hyperbolic manifolds.

We establish scale-invariant Strichartz estimates for the Schrödinger flow on any compact Lie group equipped with canonical rational metrics. In particular, full Strichartz estimates without loss for some non-rectangular tori are given. The highlights of this paper include estimates for some Weyl type sums defined on r…

The paper extends Strichartz's conjecture to spinor bundles over real hyperbolic spaces.

Improved Strichartz estimates for Schrödinger equation on manifolds with nonpositive curvature.

Study examines wave equation decay and Strichartz estimates on conic manifolds.

In this article we prove a family of local (in time) weighted Strichartz estimates with derivative losses for the Klein-Gordon equation on asymptotically de Sitter spaces and provide a heuristic argument for the non-existence of a global dispersive estimate on these spaces. The weights in the estimates depend on the ma…

Study fourth order Schrödinger equation on Cartan-Hadamard manifolds, proving existence, scattering, and blow-up results.

In this paper, we consider very rough solutions to Cauchy problem for the Einstein vacuum equations in CMC spacial harmonic gauge, and obtain the local well-posedness result in $H^s, s>2$. The novelty of our approach lies in that, without resorting to the standard paradifferential regularization over the rough, Einstei…

This is the first in a series Of papers in which we initiate the study Of very rough solutions to the initial value problem for the Einstein Vacuum equations expressed relative to wave coordinates. By very rough we mean solutions which cannot be constructed by the classical techniques Of energy estimates and Sobolev in…

The commuting vector fields approach, devised for strichartz estimates in [13], was developed for proving the local well-posedness in the Sobolev spaces $H^s$ with $s>2+\frac{2-\sqrt{3}}{2}$ for general quasi-linear wave equation in ${\mathbb R}^{1+3}$ by Klainerman and Rodnianski. Via this approach they obtained the l…

Generalizes results for Riemannian manifolds with boundary to those without.

We prove the local-in-time well-posedness for the solution of the compressible Euler equations in $3$-D, for the Cauchy data of the velocity, density and vorticity $(v,\varrho, \fw) \in H^s\times H^s\times H^{s'}$, $2<s'<s$. The classical local well-posedness result for the compressible Euler equations in $3$-D holds f…

J. Kigami has laid the foundations of what is now known as analysis on fractals, by allowing the construction of an operator of the same nature of the Laplacian, defined locally, on graphs having a fractal character. The Sierpinski gasket stands out of the best known example. It has, since then, been taken up, develope…

New estimators outperform maximum likelihood without hyper-parameter estimation.

New estimator reduces kernel mean estimation error.

Dual Bayesian Affine Estimators for Wiener-type state-space models

Enhances gradient estimates for Hermitian Monge-Ampère equations.

Paper proposes robust estimators for GANs under Wasserstein contamination.

New framework converts offline to online estimation using black-box offline estimators.

Proposes variational autoencoder for efficient MMSE estimation.

Paper improves Fisher information estimation methods.

Proposes a robust estimator for RD designs.

New estimator reduces variance in discrete random variables.

SCOPE estimator improves covariance and precision matrix estimation.

We present a multi-task learning approach to jointly estimate the means of multiple independent data sets. The proposed multi-task averaging (MTA) algorithm results in a convex combination of the single-task maximum likelihood estimates. We derive the optimal minimum risk estimator and the minimax estimator, and show t…

Obtaining more accurate equity value estimates is the starting point for stock selection, value-based indexing in a noisy market, and beating benchmark indices through tactical style rotation. Unfortunately, discounted cash flow, method of comparables, and fundamental analysis typically yield discrepant valuation estim…

The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties. Being able to efficiently estimate the variance of this estimator is very helpful to vario…

Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on discretely sampled continuo…

A new copula estimation method using classification.

This paper reviews SDR methods for multivariate response regression.

Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation procedure can be vulnerable to corrupted data points, which often pushes the estimated ratio toward infinity. In this paper, we present a robust estimator wh…

TAKDE optimizes kernel density estimation for real-time dynamic processes.

We introduce two new estimators of the bivariate Hurst exponent in the power-law cross-correlations setting -- the cross-periodogram and local $X$-Whittle estimators -- as generalizations of their univariate counterparts. As the spectrum-based estimators are dependent on a part of the spectrum taken into consideration …

Paper bridges score estimation to parameter and density estimation in DDPMs.

New method for fast volatility estimation robust to change points.

ROME improves density estimation for multi-modal, non-normal data.

Paper introduces VDE, a variance-reduced determinant estimator.

New estimator improves mutual information estimation.

Private estimation of many quantiles using differential privacy.

Paper proposes robust LAD estimators for 2D sinusoidal model, proving consistency and normality.

Combines multiple OPE estimators into a more accurate and efficient estimate.

Optimal and safe semi-supervised learning estimator for high-dimensional data.

Improved nonparametric regression with debiasing for root-n consistency.

Estimating boundaries from point clouds with improved accuracy and rigorous error estimates.