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We introduce and study a multiparameter version of the generalized counting process (GCP), where there is a possibility of finitely many arrivals simultaneously. We call it the multiparameter GCP. In a particular case, it is uniquely…

Probability · Mathematics 2025-10-06 Manisha Dhillon , Kuldeep Kumar Kataria

Within the framework of functional data analysis, we develop principal component analysis for periodically correlated time series of functions. We define the components of the above analysis including periodic, operator-valued filters,…

Methodology · Statistics 2016-12-02 Łukasz Kidziński , Piotr Kokoszka , Neda Mohammadi Jouzdani

A multivariate, stationary time series is said to be jointly regularly varying if all its finite-dimensional distributions are multivariate regularly varying. This property is shown to be equivalent to weak convergence of the conditional…

Probability · Mathematics 2007-07-27 Bojan Basrak , Johan Segers

We consider a complex-valued linear mixture model, under discrete weakly stationary processes. We recover latent components of interest, which have undergone a linear mixing. We study asymptotic properties of a classical unmixing estimator,…

Statistics Theory · Mathematics 2020-03-12 Niko Lietzén , Lauri Viitasaari , Pauliina Ilmonen

The literature on time series of functional data has focused on processes of which the probabilistic law is either constant over time or constant up to its second-order structure. Especially for long stretches of data it is desirable to be…

Methodology · Statistics 2020-07-21 Anne van Delft , Michael Eichler

Stationary graph process models are commonly used in the analysis and inference of data sets collected on irregular network topologies. While most of the existing methods represent graph signals with a single stationary process model that…

Machine Learning · Statistics 2024-02-28 Abdullah Canbolat , Elif Vural

For point patterns observed in natura, spatial heterogeneity is more the rule than the exception. In numerous applications, this can be mathematically handled by the flexible class of log Gaussian Cox processes (LGCPs); in brief, a LGCP is…

Statistics Theory · Mathematics 2019-10-10 Jiří Dvořák , Jesper Møller , Tomáš Mrkvička , Samuel Soubeyrand

A time-varying empirical spectral process indexed by classes of functions is defined for locally stationary time series. We derive weak convergence in a function space, and prove a maximal exponential inequality and a…

Statistics Theory · Mathematics 2009-02-10 Rainer Dahlhaus , Wolfgang Polonik

We consider a measurable stationary Gaussian stochastic process. A criterion for testing hypotheses about the covariance function of such a process using estimates for its norm in the space $L_p(\mathbb {T}),\,p\geq1$, is constructed.

Probability · Mathematics 2015-03-19 Yuriy Kozachenko , Viktor Troshki

We consider the rate of piecewise constant approximation to a locally stationary process $X(t),t\in [0,1]$, having a variable smoothness index $\alpha(t)$. Assuming that $\alpha(\cdot)$ attains its unique minimum at zero and satisfies the…

Probability · Mathematics 2015-11-19 Enkelejd Hashorva , Mikhail Lifshits , Oleg Seleznjev

Multivariate processes with long-range dependent properties are found in a large number of applications including finance, geophysics and neuroscience. For real data applications, the correlation between time series is crucial. Usual…

Statistics Theory · Mathematics 2015-11-02 Sophie Achard , Irène Gannaz

Estimating the covariance structure of multivariate time series is a fundamental problem with a wide-range of real-world applications -- from financial modeling to fMRI analysis. Despite significant recent advances, current state-of-the-art…

Machine Learning · Computer Science 2021-02-12 Hrayr Harutyunyan , Daniel Moyer , Hrant Khachatrian , Greg Ver Steeg , Aram Galstyan

We develop an estimator for the high-dimensional covariance matrix of a locally stationary process with a smoothly varying trend and use this statistic to derive consistent predictors in non-stationary time series. In contrast to the…

Methodology · Statistics 2020-01-08 Holger Dette , Weichi Wu

In this paper, we show that the mixed fractional Poisson process (MFPP) exhibits the long-range dependence (LRD) property. It is proved by establishing an asymptotic result for the covariance of inverse mixed stable subordinator. Also, it…

Probability · Mathematics 2021-07-28 K. K. Kataria , M. Khandakar

The analysis of nonstationary time series is of great importance in many scientific fields such as physics and neuroscience. In recent years, Gaussian process regression has attracted substantial attention as a robust and powerful method…

Machine Learning · Statistics 2016-11-01 Luca Ambrogioni , Eric Maris

A particle system is a family of i.i.d. stochastic processes with values translated by Poisson points. We obtain conditions that ensure the stationarity in time of the particle system in R^d and in some cases provide a full characterisation…

Probability · Mathematics 2013-11-05 Ilya Molchanov , Kaspar Stucki

We consider a discrete time dynamic system described by a difference equation with periodic coefficients and with additive stochastic noise. We investigate the possibility of the periodicity for the solution. In particular, we found…

Dynamical Systems · Mathematics 2013-09-02 Alexandra Rodkina , Nikolai Dokuchaev , John Appleby

The paper deals with Hilbert space valued fields over any locally compact Abelian group G, in particular over G = Z^n x R^m, which are periodically correlated (PC) with respect to a closed subgroup of G. PC fields can be regarded as…

Statistics Theory · Mathematics 2013-07-10 Dominique Dehay , Harry L. Hurd , Andrzej Makagon

We introduce a multistable subordinator, which generalizes the stable subordinator to the case of time-varying stability index. This enables us to define a multifractional Poisson process. We study properties of these processes and…

Probability · Mathematics 2014-09-05 Ilya Molchanov , Kostiantyn Ralchenko

We show that functions of type $X_n = P[Z^n]$, where $P[t]$ is a periodic function and $Z$ is a generic real number, can produce sequences such that any string of values $X_{s}, X_{s+1}, ...,X_{s+m}$ is deterministically independent of past…

Chaotic Dynamics · Physics 2009-11-10 L. Trujillo , J. J. Suarez , J. A. Gonzalez