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We discuss joint temporal and contemporaneous aggregation of $N$ independent copies of random-coefficient AR(1) process driven by i.i.d. innovations in the domain of normal attraction of an $\alpha$-stable distribution, $0< \alpha \le 2$,…

Statistics Theory · Mathematics 2020-05-01 Vytautė Pilipauskaitė , Viktor Skorniakov , Donatas Surgailis

It is well-known that random-coefficient AR(1) process can have long memory depending on the index $\beta$ of the tail distribution function of the random coefficient, if it is a regularly varying function at unity. We discuss estimation of…

Statistics Theory · Mathematics 2019-09-23 Remigijus Leipus , Anne Philippe , Vytaute Pilipauskaite , Donatas Surgailis

We discuss joint temporal and contemporaneous aggregation of $N$ independent copies of strictly stationary AR(1) and INteger-valued AutoRegressive processes of order 1 (INAR(1)) with random coefficient $\alpha \in (0, 1)$ and idiosyncratic…

Probability · Mathematics 2016-01-19 Fanni Nedényi , Gyula Pap

We discuss joint temporal and contemporaneous aggregation of $N$ independent copies of AR(1) process with random-coefficient $a \in [0,1)$ when $N$ and time scale $n$ increase at different rate. Assuming that $a$ has a density, regularly…

Statistics Theory · Mathematics 2013-10-23 Vytaute Pilipauskaite , Donatas Surgailis

We discuss nonparametric estimation of the distribution function $G(x)$ of the autoregressive coefficient $a \in (-1,1)$ from a panel of $N$ random-coefficient AR(1) data, each of length $n$, by the empirical distribution function of lag 1…

Statistics Theory · Mathematics 2016-10-06 Remigijus Leipus , Anne Philippe , Vytautė Pilipauskaitė , Donatas Surgailis

A general asymptotic theory is given for the panel data AR(1) model with time series independent in different cross sections. The theory covers the cases of stationary process, nearly non-stationary process, unit root process, mildly…

Applications · Statistics 2016-11-15 Jianfei Shen , Tianxiao Pang

This paper studies a class of linear panel models with random coefficients. We do not restrict the joint distribution of the time-invariant unobserved heterogeneity and the covariates. We investigate identification of the average partial…

Econometrics · Economics 2022-11-21 Louise Laage

Panel count data is common when the study subjects are exposed to recurrent events, observed only at discrete time points. In this article, we consider the regression analysis of panel count data with multiple modes of recurrence. We…

Methodology · Statistics 2021-07-06 Sreedevi E. P. , Sankaran P. G.

We introduce a model for a growing random graph based on simultaneous reproduction of the vertices. The model can be thought of as a generalisation of the reproducing graphs of Southwell and Cannings and Bonato et al to allow for a random…

Probability · Mathematics 2011-04-20 Jonathan Jordan

This paper considers a first-order autoregressive panel data model with individual-specific effects and heterogeneous autoregressive coefficients defined on the interval (-1,1], thus allowing for some of the individual processes to have…

Econometrics · Economics 2024-06-26 M. Hashem Pesaran , Liying Yang

Suppose that $k$ series, all having the same autocorrelation function, are observed in parallel at $n$ points in time or space. From a single series of moderate length, the autocorrelation parameter $\beta$ can be estimated with limited…

Statistics Theory · Mathematics 2008-10-23 Peter McCullagh

The paper considers high frequency sampled multivariate continuous-time ARMA (MCARMA) models, and derives the asymptotic behavior of the sample autocovariance function to a normal random matrix. Moreover, we obtain the asymptotic behavior…

Statistics Theory · Mathematics 2015-08-10 Vicky Fasen

We introduce the notion of symmetric covariation, which is a new measure of dependence between two components of a symmetric $\alpha$-stable random vector, where the stability parameter $\alpha$ measures the heavy-tailedness of its…

Statistics Theory · Mathematics 2021-05-20 Yujia Ding , Qidi Peng

This paper investigates the asymptotics of eigenstructure of sample covariance matrix under the spiked covariance matrix model in ultra-high-dimensional settings, where the dimensionality can grow much faster than the sample size with $ p…

Statistics Theory · Mathematics 2026-04-30 Wonjun Seo

We study the joint limit distribution of the $k$ largest eigenvalues of a $p\times p$ sample covariance matrix $XX^\T$ based on a large $p\times n$ matrix $X$. The rows of $X$ are given by independent copies of a linear process,…

Probability · Mathematics 2012-10-31 Richard A. Davis , Oliver Pfaffel , Robert Stelzer

We consider random rectangles in $\mathbb{R}^2$ that are distributed according to a Poisson random measure, i.e., independently and uniformly scattered in the plane. The distributions of the length and the width of the rectangles are…

Probability · Mathematics 2018-06-29 Frank Aurzada , Sebastian Schwinn

We examine the asymptotic behaviour of the sample autocovariance in a continuous-time moving average model with long-range dependence. We show that it is either asymptotically Rosenblatt distributed or stable distributed. This shows that…

Probability · Mathematics 2015-11-24 Felix Spangenberg

Let \{$X_{ij}$\}, $i,j=...,$ be a double array of i.i.d. complex random variables with $EX_{11}=0,E|X_{11}|^2=1$ and $E|X_{11}|^4<\infty$, and let $A_n=\frac{1}{N}T_n^{{1}/{2}}X_nX_n^*T_n^{{1}/{2}}$, where $T_n^{{1}/{2}}$ is the square root…

Probability · Mathematics 2007-08-22 Z. D. Bai , B. Q. Miao , G. M. Pan

The aim of this paper is to provide conditions which ensure that the affinely transformed partial sums of a strictly stationary process converge in distribution to an infinite variance stable distribution. Conditions for this convergence to…

Probability · Mathematics 2011-10-20 Katarzyna Bartkiewicz , Adam Jakubowski , Thomas Mikosch , Olivier Wintenberger

This article studies the limiting behavior of a class of robust population covariance matrix estimators, originally due to Maronna in 1976, in the regime where both the number of available samples and the population size grow large. Using…

Information Theory · Computer Science 2016-11-18 Romain Couillet , Frederic Pascal , Jack W. Silverstein
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