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We give an overview of basic methods that can be used for obtaining asymptotic expansions of integrals: Watson's lemma, Laplace's method, the saddle point method, and the method of stationary phase. Certain developments in the field of…

Classical Analysis and ODEs · Mathematics 2013-08-08 Nico M. Temme

A functional limit theorem is established for the partial-sum process of a class of stationary sequences which exhibit both heavy tails and long-range dependence. The stationary sequence is constructed using multiple stochastic integrals…

Probability · Mathematics 2020-04-09 Shuyang Bai , Takashi Owada , Yizao Wang

The main purpose of this paper is to study and characterize the existing of general asymptotic regional gradient observer which observe the current gradient state of the original system in connection with gradient strategic sensors. Thus,…

Optimization and Control · Mathematics 2020-05-08 Asmaa N. Al-Janabi , Raheam A. Al-Saphory , Naseif J. Al-Jawari

Feature selection procedures for spatial point processes parametric intensity estimation have been recently developed since more and more applications involve a large number of covariates. In this paper, we investigate the setting where the…

Methodology · Statistics 2017-12-29 Achmad Choiruddin , Jean-François Coeurjolly , Frédérique Letué

The popular generalized additive model framework is extended to allow both the mean curves and the response distribution to be nonparametric. The approach is demonstrated to be a flexible yet parsimonious tool for data analysis in its own…

Methodology · Statistics 2017-09-18 Alan Huang , Nanxi Zhang

Many spatial processes exhibit nonstationary features. We estimate a variance function from a single process observation where the errors are nonstationary and correlated. We propose a difference-based approach for a one-dimensional…

Methodology · Statistics 2016-05-24 Eunice J. Kim , Zhengyuan Zhu

New algorithms for construction of asymptotic expansions for stationary distributions of nonlinearly perturbed semi-Markov processes with finite phase spaces are presented. These algorithms are based on a special technique of sequential…

Probability · Mathematics 2016-03-16 Dmitrii Silvestrov , Sergei Silvestrov

This invited paper proposes and discusses several Bayesian attempts at nonparametric and semiparametric density estimation. The main categories of these ideas are as follows: 1) Build a nonparametric prior around a given parametric model.…

Statistics Theory · Mathematics 2026-04-23 Nils Lid Hjort

The second author had previously obtained explicit generating functions for moments of characteristic polynomials of permutation matrices (n points). In this paper, we generalize many aspects of this situation. We introduce random shifts of…

Probability · Mathematics 2009-11-23 Paul-Olivier Dehaye , Dirk Zeindler

This paper introduces the generalized Hausman test as a novel method for detecting non-normality of the latent variable distribution of unidimensional Item Response Theory (IRT) models for binary data. The test utilizes the pairwise maximum…

Methodology · Statistics 2024-02-14 Lucia Guastadisegni , Silvia Cagnone , Irini Moustaki , Vassilis Vasdekis

This work is a continuation of [7]. We consider a continuous-time birth-and-death process in which the transition rates have an asymptotical power-law dependence upon the position of the process. We establish rough exponential asymptotic…

Probability · Mathematics 2019-11-12 A. V. Logachov , Y. M. Suhov , N. D. Vvedenskaya , A. A. Yambartsev

This paper studies the asymptotic behavior of several central objects in Dunkl theory as the dimension of the underlying space grows large. Our starting point is the observation that a recent result from the random matrix theory literature…

Probability · Mathematics 2023-05-24 Jiaoyang Huang , Colin McSwiggen

We study Hermitian random matrix models with an external source matrix which has equispaced eigenvalues, and with an external field such that the limiting mean density of eigenvalues is supported on a single interval as the dimension tends…

Mathematical Physics · Physics 2013-06-25 Tom Claeys , Dong Wang

We consider the diffusion of independent particles experiencing random accelerations by a space- and time-dependent force as well as viscous damping. This model can exhibit several asymptotic behaviours, depending upon the limiting cases…

Chaotic Dynamics · Physics 2012-06-13 B. Mehlig , M. Wilkinson , V. Bezuglyy , K. Gustavsson , K. Nakamura

Integral transform method (Fourier or Laplace transform, etc) is more often effective to do the theoretical analysis for the stochastic processes. However, for the time-space coupled cases, e.g., L\'evy walk or nonlinear cases, integral…

Statistical Mechanics · Physics 2020-03-13 Pengbo Xu , Weihua Deng , Trifce Sandev

We study the problem of nonparametric estimation of the linear multiplier function $\theta(t)$ for processes satisfying stochastic differential equations of the type $$dX_t=\theta(t) X_tdt+ \epsilon dZ^{q,H}_t, X_0=x_0, 0\leq t \leq T$$…

Statistics Theory · Mathematics 2026-02-19 B. L. S. Prakasa Rao

We study the dynamical features of Maggiore's generalised commutation relations. We focus on their generality and, in particular, their dependence on the Hamiltonian H. We derive the generalisation of the Planck's law for black body…

High Energy Physics - Theory · Physics 2015-06-26 S. Kalyana Rama

We consider a stationary and isotropic spatial point process, whose a realisation is observed within a large window. In order to predict its local intensity, we propose to define the first- and second-order characteristics of a random…

Methodology · Statistics 2015-12-16 Edith Gabriel , Florent Bonneu , Pascal Monestiez , Joel Chadoeuf

Non-Hermiticity gives rise to unique topological phases without Hermitian analogs. However, the effective field theory has yet to be established. Here, we develop a field-theoretical description of the intrinsic non-Hermitian topological…

Mesoscale and Nanoscale Physics · Physics 2021-06-01 Kohei Kawabata , Ken Shiozaki , Shinsei Ryu

The hierarchical Dirichlet process is a discrete random measure used as a prior in Bayesian nonparametrics and motivated by the study of groups of clustered data. We study the asymptotic behavior of the power sum symmetric polynomials for…

Probability · Mathematics 2025-08-29 Shui Feng , J. E. Paguyo