arXiv research

Paper defines new risk measures for elliptical distributions.

Paper extends LME models to allow sign constraints on coefficients with SDTN random effects.

The paper calculates moments and conditional risks for skewed elliptical distributions.

Computing partition functions, the normalizing constants of probability distributions, is often hard. Variants of importance sampling give unbiased estimates of a normalizer Z, however, unbiased estimates of the reciprocal 1/Z are harder to obtain. Unbiased estimates of 1/Z allow Markov chain Monte Carlo sampling of "d…

Doubly-stochastic normalization improves robustness to heteroskedastic noise.

The present paper regards the volume function of a doubly truncated hyperbolic tetrahedron. Starting from the previous results of J. Murakami, U. Yano and A. Ushijima, we have developed a unified approach to express the volume in different geometric cases via dilogarithm functions and to treat properly the many analyti…

The article enumerates doubly symmetric diagrams for knots up to 18 crossings.

The paper examines Einstein doubly warped product manifolds with a semi-symmetric metric connection.

We consider free and proper cotangent-lifted symmetries of Hamiltonian systems. For the special case of G = SO(3), we construct symplectic slice coordinates around an arbitrary point. We thus obtain a parametrisation of the phase space suitable for the study of dynamics near relative equilibria, in particular for the B…

Improved off-policy evaluation for MDPs with weak distributional overlap.

The twisting number of a ribbon knot is at least as large as its doubly slice genus.

We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate various types of nonlinearities within a unified framework, including sigmoid, tanh and ReLU, the most commonly used nonlinearities in neural…

Corrects mismatch in consistency of nuisance estimators for doubly robust methods.

Gradually Truncated Log-normal distribution - Size distribution of firms Abstract Many natural and economical phenomena are described through power law or log- normal distributions. In these cases, probability decreases very slowly with step size compared to normal distribution. Thus it is essential to cut-off these di…

A new algorithm speeds up elliptical slice sampling for truncated multivariate normals.

We consider two eigenvalue problems for Laplacian on some specific doubly connected domain. In particular, we study the following two eigenvalue problems. Let $B_1$ be an open ball in $\mathbb{R}^n$ and $B_0$ be a ball contained in $B_1$. Let $ν$ be the outward unit normal on $\partial B_1$. Then the first eigenvalue o…

Proves volume conjecture for double twist knots using complexified tetrahedrons.

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic procedures for expected value and the variance of the truncated-normal distribu…

This paper addresses challenges in flexibly modeling multimodal data that lie on constrained spaces. Such data are commonly found in spatial applications, such as climatology and criminology, where measurements are restricted to a geographical area. Other settings include domains where unsuitable recordings are discard…

Paper proposes Sinkformers for Transformers with doubly stochastic attention.

The development of global sensitivity analysis of numerical model outputs has recently raised new issues on 1-dimensional Poincaré inequalities. Typically two kind of sensitivity indices are linked by a Poincaré type inequality, which provide upper bounds of the most interpretable index by using the other one, cheaper …

The paper finds bounds on shortest dense curves on surfaces.

Study identifies prime strongly positive amphicheiral knots with double symmetry.

Truncated densities are probability density functions defined on truncated domains. They share the same parametric form with their non-truncated counterparts up to a normalizing constant. Since the computation of their normalizing constants is usually infeasible, Maximum Likelihood Estimation cannot be easily applied t…

Study spherical doubly warped spacetimes for stellar collapse and cosmology.

We propose an efficient method for estimating covariate effects in doubly-stochastic spatial models.

We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate normal distribution from truncated samples. Truncated samples from a $d$-variate normal ${\cal N}(\mathbfμ,\mathbfΣ)$ means a samples is only re…

CPME embeds counterfactual outcomes in RKHS for flexible policy evaluation.

New bounds on self-normalized martingales improve online linear regression performance.

We accelerate CNF by reducing ODE truncation errors with polynomial regularization.

For a Riemannian submersion from a simple compact Lie group with a bi-invariant metric, we prove the action of its holonomy group on the fibers is transitive. As a step towards classifying Riemannian submersions with totally geodesic fibers, we consider the parameterized surface induced by lifting a base geodesic to po…

Robustly infers manifold density and geometry under high-dimensional noise.

The paper examines gradient ρ-Einstein solitons on specific manifolds and spacetimes.

The paper improves asset allocation using a skew-normal distribution in the Black-Litterman model.

Proposes a new method to estimate Bayesian neural network depth.

Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations. However, they are also known for their limited modeling abilities, due to the Gaussian assumption. In this paper, we introduce a novel va…

Paper simplifies data carving inference with a parametric distribution.

A (1,1) knot K in a 3-manifold M is a knot that intersects each solid torus of a genus 1 Heegaard splitting of M in a single trivial arc. Choi and Ko developed a parameterization of this family of knots by a four-tuple of integers, which they call Schubert's normal form. This article presents an algorithm for construct…

Study develops smart contract framework for procurement under demand variability.

A large number of statistical models are "doubly-intractable": the likelihood normalising term, which is a function of the model parameters, is intractable, as well as the marginal likelihood (model evidence). This means that standard inference techniques to sample from the posterior, such as Markov chain Monte Carlo (…

Improved Transformer performance by addressing 'explaining away' effect.

Let $\mathcal{K}$ be the space of properly embedded minimal tori in quotients of $\R^3$ by two independent translations, with any fixed (even) number of parallel ends. After an appropriate normalization, we prove that $\mathcal{K}$ is a 3-dimensional real analytic manifold that reduces to the finite coverings of the ex…

New method for estimating parameters in inverse problems using double robustness.

Paper tackles causal inference with partially labeled data, introducing robust methods.

Improved estimators for causal inference using cross-fitting and undersmoothing.

Generalized score matching for densities on general domains.

Closed-form formulas for path-independent options in a specific Lévy model.

Paper proposes a new DR estimator for adaptive experiments with improved performance.