Results 51 to 60 of about 39,038 (168)

Quantisations of piecewise affine maps on the torus and their quantum limits

, 2007
For general quantum systems the semiclassical behaviour of eigenfunctions in relation to the ergodic properties of the underlying classical system is quite difficult to understand.
A.B. Katok, Cheng-Hung Chang, D. Kelmer, D. Kelmer, E. Bogomolny, E. Lindenstrauss, F. Bonechi, F. Faure, F. Faure, H.B. Keynes, J. Marklof, J. Marklof, L. Rosenzweig, M. Keane, N. Anantharaman, N.A. Friedman, O. Giraud, P. Gérard, P. Kurlberg, Roman Schubert, S. Ferenczi, S. Zelditch, Serge Troubetzkoy, Tyll Krüger, W.A. Veech, W.A. Veech, Y. Colin de Verdière, Z. Rudnick   +27 more
core   +2 more sources

Markov Determinantal Point Process for Dynamic Random Sets

Journal of Time Series Analysis, EarlyView.
ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two
Christian Gouriéroux, Yang Lu
wiley   +1 more source

Rational weak mixing in infinite measure spaces

, 2013
Rational weak mixing is a measure theoretic version of Krickeberg's strong ratio mixing property for infinite measure preserving transformations. It requires "{\tt density}" ratio convergence for every pair of measurable sets in a dense hereditary ring ...
Aaronson, Bingham, Friedman, JON AARONSON, Karlin, Krickeberg   +5 more
core   +1 more source

Tests for Changes in Count Time Series Models With Exogenous Covariates

Journal of Time Series Analysis, EarlyView.
ABSTRACT We deal with a parametric change in models for count time series with exogenous covariates specified via the conditional distribution, i.e., with integer generalized autoregressive conditional heteroscedastic models with covariates (INGARCH‐X).
Šárka Hudecová, Marie Hušková
wiley   +1 more source

On orbits under ergodic measure-preserving transformations [PDF]

Transactions of the American Mathematical Society, 1965
Let \(I\) be the unit interval with Lebesgue measure and let \(T\) be a 1-1 measure-preserving ergodic transformation mapping \(I\) onto \(I\). Intuitively one feels that the orbit \(\{T^nx\}\) of a ``general'' point \(x\in I\) should somehow determine \(T\). This notion is made precise in this paper.
openaire   +2 more sources

On Exponential‐Family INGARCH Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT A range of integer‐valued generalised autoregressive conditional heteroscedastic (INGARCH) models have been proposed in the literature, including those based on conditional Poisson, negative binomial and Conway‐Maxwell‐Poisson distributions. This note considers a larger class of exponential‐family INGARCH models, showing that maximum empirical
Alan Huang, Thomas Fung, Kyle Macaskill, Rowan Aukes   +3 more
wiley   +1 more source

Spectra of ergodic transformations

Journal of Functional Analysis, 1974
Let \(\mathcal R\) be a von Neumann algebra acting on a Hilbert space \(\mathcal H\) and \(G\) a locally compact abelian group. Suppose \(g\to U_g\), is a strongly continuous unitary representation of \(G\) on \(\mathcal H\) implementing an ergodic group of automorphisms \(\alpha_g\) of \(\mathcal R\).
openaire   +1 more source

Time‐Varying Dispersion Integer‐Valued GARCH Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT We introduce a general class of INteger‐valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing simultaneously time‐varying mean and dispersion parameters. We call such models time‐varying dispersion INGARCH (tv‐DINGARCH) models.
Wagner Barreto‐Souza, Luiza S. C. Piancastelli, Konstantinos Fokianos, Hernando Ombao   +3 more
wiley   +1 more source

Estimation of Change Points for Non‐Linear (Auto‐)Regressive Processes Using Neural Network Functions

Journal of Time Series Analysis, EarlyView.
ABSTRACT In this paper, we propose a new test for the detection of a change in a non‐linear (auto‐)regressive time series as well as a corresponding estimator for the unknown time point of the change. To this end, we consider an at‐most‐one‐change model and approximate the unknown (auto‐)regression function by a neural network with one hidden layer. It
Claudia Kirch, Stefanie Schwaar
wiley   +1 more source

Random power series generated by ergodic transformations [PDF]

Transactions of the American Mathematical Society, 1986
Generalizing classical studies of power series with sequences of independent random variables as coefficients, we study series of the forms \[ g x , ϕ ( z ) = ∑ n = 0
Halchin, Judy, Petersen, Karl
openaire   +1 more source