Research
On-device research index

arXiv research

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.

168,657 papers · 148 categories

Trend · papers per month

89178266355 · May 202619922001200920172026
48 results for asymptotic level

Study on Chern-Simons theory at generic levels, revealing universal resurgent structure.

problem Analyzing Chern-Simons theory at generic levels with small boundary holonomy.
method Examined resurgent structure of state integral models on knot complements with generic discrete level.
result Resurgent structure is universal, independent of the level kk.

Researchers prove a Penrose inequality for spacetime with specific conditions.

problem Establishing mass lower bounds for spacetime with specific asymptotic conditions.
method Combining harmonic level set approach, Jang equation, and stability techniques.
result Proof of Penrose inequality with universal constant and minimal area requirement.

Study equi-affine invariants for convex domains with asymptotes.

problem Understanding geometric properties of convex domains with specific asymptotes.
method Introducing equi-affine invariants by averaging tropical structures.
result Proving a limiting description of level sets for unbounded domains with two non-parallel asymptotes.

Estimates population mean from user-level data with privacy, accounting for heterogeneity.

problem Heterogeneous user data with varying numbers of data points and distributions.
method Simple model of heterogeneous user data, differential privacy mechanism for estimation.
result Asymptotic optimality of the proposed estimator and general lower bounds on error.

Paper examines risk measure expansions under FGM dependence, improving accuracy at extreme levels.

problem Capturing higher-order tail behavior and dependence effects in risk measures.
method Second-order asymptotic expansions using extreme value theory and regular variation theory.
result Second-order approximations reduce approximation errors, especially at extreme confidence levels.

The paper constructs hypersurfaces translating under powers of Gauss curvature.

problem Existence of hypersurfaces translating under powers of Gauss curvature.
method Constructs complete convex hypersurfaces in R^(n+1) translating under flow by powers of Gauss curvature.
result Existence of translators whose level set converges to various shapes like sphere, simplex, and hypercube.

Develops privacy-preserving methods for longitudinal linear regression.

problem Protecting individual information in longitudinal data with privacy-preserving statistics.
method Proposes a user-level private regression estimator and a privatized covariance estimator for longitudinal linear regression under user-level differential privacy.
result Establishes theoretical guarantees for practical user-level differential privacy estimation and inference in longitudinal linear regression.

We study the asymptotic behaviour of 1-parameter subgroups with respect to Hofer's metric when the underlying symplectic manifold is an open surface of infinite area. We prove that, depending on the topology of the level sets of the Hamiltonian H, the distance either is bounded or behaves asymptotically linear. Moreove…

1999-05-10abs ↗pdf ↗

We study an optimal investment control problem for an insurance company. The surplus process follows the Cramer-Lundberg process with perturbation of a Brownian motion. The company can invest its surplus into a risk free asset and a Black-Scholes risky asset. The optimization objective is to minimize the probability of…

2015-02-08abs ↗pdf ↗

The paper studies eigenvalues in gaps of the essential spectrum of a Bochner-Schrödinger operator.

problem Eigenvalue distribution in gaps of the essential spectrum of the Bochner-Schrödinger operator.
method Trace asymptotics formula and Weyl type asymptotic formula for eigenvalue counting function.
result The spectrum of HpH_{p} in the gap is discrete.

The paper addresses the gap between theoretical and practical confidence set widths in universal inference.

problem Inference procedures can be overly conservative, leading to wider confidence sets than expected.
method The authors identify the source of asymptotic conservativeness and propose a remedy based on studentization and bias correction.
result The proposed method achieves exact asymptotic coverage at the nominal 1α1-α level, even under model misspecification.

We characterize the asymptotic performance of nonparametric one- and two-sample testing. The exponential decay rate or error exponent of the type-II error probability is used as the asymptotic performance metric, and an optimal test achieves the maximum rate subject to a constant level constraint on the type-I error pr…

2019-08-27abs ↗pdf ↗

We study the asymptotic behavior of the difference ΔραX,Y:=ρα(X+Y)ρα(X)Δρ^{X, Y}_α:= ρ_α(X + Y) - ρ_α(X) as α1α\rightarrow 1, where ραρ_α is a risk measure equipped with a confidence level parameter 0<α<10 < α< 1, and where XX and YY are non-negative random variables whose tail probability functions are regularly varying. The case where …

2017-11-20abs ↗pdf ↗

Researchers compute trace formula for magnetic Laplacian on hyperbolic surfaces.

problem Analyzing the magnetic Laplacian on compact hyperbolic surfaces.
method Computed the trace formula for magnetic Laplacian energies above the Mane critical level.
result Asymptotic behavior of trace formula coefficients near the Mane critical level.

This paper presents the first theoretical results showing that stable identification of overcomplete μμ-coherent dictionaries ΦRd×KΦ\in \mathbb{R}^{d\times K} is locally possible from training signals with sparsity levels SS up to the order O(μ2)O(μ^{-2}) and signal to noise ratios up to O(d)O(\sqrt{d}). In particular the di…

2014-01-24abs ↗pdf ↗

New asymptotic e-values improve inference by eliminating data-dependent scaling inefficiency.

problem Data-dependent scaling inefficiency in existing asymptotic e-values.
method Drawing on Bentkus's near-optimal concentration inequalities, introduce Bentkus-type asymptotic e-values.
result Bentkus-type asymptotic e-values consistently deliver sharper inference than existing alternatives.

Consider the Chern-Simons topological quantum field theory with gauge group SU(2) and level k. Given a knot in the 3-sphere, this theory associates to the knot exterior an element in a vector space. We call this vector the knot state and study its asymptotic properties when the level is large. The latter vector space b…

2011-07-08abs ↗pdf ↗

The asymptotic lattices and their transformations are studied within the line geometry approach. It is shown that the discrete asymptotic nets are represented by isotropic congruences in the Plucker quadric. On the basis of the Lelieuvre-type representation of asymptotic lattices and of the discrete analog of the Mouta…

1999-09-16abs ↗pdf ↗

Study resolves conjecture on overparameterized linear models' generalization.

problem Asymptotic generalization of multiclass classification with overparameterized models.
method Gaussian covariates bi-level model, Hanson-Wright inequality variant.
result Min-norm interpolating classifier can be suboptimal compared to noninterpolating classifiers.

The goal of this paper is to measure the non-convexity of compact and smooth connected components of real algebraic plane curves. We study these curves first in a general setting and then in an asymptotic one. In particular, we consider sufficiently small levels of a real bivariate polynomial in a small enough neighbou…

2019-07-19abs ↗pdf ↗

A new algorithm estimates mean under varying user data sizes with local differential privacy.

problem Mean estimation with user-level local differential privacy under varying data sizes.
method Distribution-aware mean estimation algorithm for users with varying data sizes.
result Upper and lower bounds on the worst-case risk for mean estimation are derived.

The study finds a trade-off between model size, test loss, and training loss for linear predictors.

problem Finding the optimal balance between model size, test loss, and training loss for linear predictors.
method Established an algorithm and distribution-independent trade-off using non-asymptotic analysis.
result Models with low test loss are either classical (close to noise level training loss) or modern (large number of parameters).

New bounds on efficiency for conformalized regression methods.

problem Efficiency of conformal prediction in regression models.
method Non-asymptotic bounds on prediction set length for conformalized quantile and median regression.
result Identifies phase transitions in convergence rates across different regimes of miscoverage level.

Correlation mixtures of elliptical copulas arise when the correlation parameter is driven itself by a latent random process. For such copulas, both penultimate and asymptotic tail dependence are much larger than for ordinary elliptical copulas with the same unconditional correlation. Furthermore, for Gaussian and Stude…

2009-12-17abs ↗pdf ↗

Study bounds noise level in linear regression with dependent data.

problem Analyzing noise level in linear regression with dependent data.
method Derive upper bounds for random design linear regression with ββ-mixing data, without realizability assumptions.
result Correctly recovers the noise level of the problem, exhibiting graceful degradation with misspecification.

Semiparametric method removes bias in functional bilevel gradient estimation.

problem First-order bias in plug-in hypergradient when lower-level problem is nonparametric.
method Semiparametric debiasing theory based on efficient influence function leads to cross-fitted orthogonal hypergradient estimator.
result Asymptotic normality and uniform control over outer parameter established for the estimator.

This paper shows how to combine optimal tests into log-optimal processes.

problem How to combine optimal sequential tests into log-optimal processes.
method Using a new class of WAIT e-processes, the paper aggregates asymptotically optimal sequential tests into asymptotically log-optimal processes.
result It is possible to aggregate asymptotically optimal sequential tests into asymptotically log-optimal e-processes.