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Nonparanormal models describe the joint distribution of multivariate responses via latent Gaussian, and thus parametric, copulae while allowing flexible nonparametric marginals. Some aspects of such distributions, for example conditional…

Methodology · Statistics 2025-12-16 Torsten Hothorn

The purpose of this article is to develop a general parametric estimation theory that allows the derivation of the limit distribution of estimators in non-regular models where the true parameter value may lie on the boundary of the…

Statistics Theory · Mathematics 2022-11-28 Junichiro Yoshida , Nakahiro Yoshida

We define a new model structure on the category of small categories, which is intimately related to the notion of coverings and fundamental groups of small categories. Fibrant objects in the model structure coincide with groupoids, and the…

Category Theory · Mathematics 2012-05-08 Kohei Tanaka

We study the regularity of solutions of parabolic fully nonlinear nonlocal equations. We proof Holder regularity in space and time and for translation invariant equations and under different assumptions on the kernels Holder regularity for…

Analysis of PDEs · Mathematics 2012-05-17 Héctor A. Chang Lara , Gonzalo Dávila

Parametric distributions are an important part of statistics. There is now a voluminous literature on different fascinating formulations of flexible distributions. We present a selective and brief overview of a small subset of these…

Statistics Theory · Mathematics 2020-05-15 Sharon X. Lee , Geoffrey J. McLachlan

We study Milner's lambda-calculus with partial substitutions. Particularly, we show confluence on terms and metaterms, preservation of \b{eta}-strong normalisation and characterisation of strongly normalisable terms via an intersection…

Logic in Computer Science · Computer Science 2023-12-21 Delia Kesner , Shane Ó Conchúir

The lambda calculus with constructors is an extension of the lambda calculus with variadic constructors. It decomposes the pattern-matching a la ML into a case analysis on constants and a commutation rule between case and application…

Logic in Computer Science · Computer Science 2012-03-06 Barbara Petit

What does it mean for a causal structure to be `unknown'? Can we even talk about `repetitions' of an experiment without prior knowledge of causal relations? And under what conditions can we say that a set of processes with arbitrary,…

Quantum Physics · Physics 2025-02-12 Fabio Costa , Jonathan Barrett , Sally Shrapnel

Multivariate peaks over thresholds modeling based on generalized Pareto distributions has up to now only been used in few and mostly 2-dimensional situations. This paper contributes theoretical understanding, physically based models,…

Probability · Mathematics 2017-05-04 Holger Rootzén , Johan Segers , Jennifer L. Wadsworth

The properties of the normal distribution under linear transformation, as well the easy way to compute the covariance matrix of marginals and conditionals, offer a unique opportunity to get an insight about several aspects of uncertainties…

Data Analysis, Statistics and Probability · Physics 2018-02-12 Giulio D'Agostini

We prove a general theorem on the stochastic convergence of appropriately renormalized models arising from nonlinear stochastic PDEs. The theory of regularity structures gives a fairly automated framework for studying these problems but…

Probability · Mathematics 2018-01-23 Ajay Chandra , Martin Hairer

In many regression settings the unknown coefficients may have some known structure, for instance they may be ordered in space or correspond to a vectorized matrix or tensor. At the same time, the unknown coefficients may be sparse, with…

Methodology · Statistics 2023-04-28 Maryclare Griffin , Peter D. Hoff

Following \cite{B2}, we introduce a notion of para-products associated to a semi-group. We do not use Fourier transform arguments and the background manifold is doubling, endowed with a sub-laplacian structure. Our main result is a…

Analysis of PDEs · Mathematics 2011-10-04 Frédéric Bernicot , Yannick Sire

New measures for the quantization of systems with constraints are discussed and applied to several examples, in particular, examples of alternative but equivalent formulations of given first-class constraints, as well as a comparison of…

Quantum Physics · Physics 2007-05-23 John R. Klauder

We introduce an approach to study certain singular PDEs which is based on techniques from paradifferential calculus and on ideas from the theory of controlled rough paths. We illustrate its applicability on some model problems like…

Probability · Mathematics 2017-08-16 Massimiliano Gubinelli , Peter Imkeller , Nicolas Perkowski

The idea of counting the number of satisfying truth assignments (models) of a formula by adding random parity constraints can be traced back to the seminal work of Valiant and Vazirani, showing that NP is as easy as detecting unique…

Logic in Computer Science · Computer Science 2017-08-01 Dimitris Achlioptas , Panos Theodoropoulos

In this paper, we provide a complete regularity analysis for an abstract system of coupled hyperbolic and parabolic equations in a complex Hilbert space. We are able to decompose the unit square of the parameters into three parts where the…

Analysis of PDEs · Mathematics 2014-04-25 Jianghao Hao , Zhuangyi Liu , Jiongmin Yong

Castelnuovo-Mumford regularity and any extended degree function can be thought of as complexity measures for the structure of finitely generated graded modules. A recent result of Doering, Gunston, Vasconcelos shows that both can be…

Commutative Algebra · Mathematics 2007-05-23 Uwe Nagel

We introduce a notion of a filtered model structure and use this notion to produce various model structures on pro-categories. This framework generalizes several known examples. We give several examples, including a homotopy theory for…

Algebraic Topology · Mathematics 2007-05-23 Halvard Fausk , Daniel C. Isaksen

We propose two bounded comparison metrics that may be implemented to arbitrary dimensions in regression tasks. One quantifies the structure of uncertainty and the other quantifies the distribution of uncertainty. The structure metric…

Machine Learning · Computer Science 2022-03-10 Ethan Pickering , Themistoklis P. Sapsis
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