Self-normalized partial sums of heavy-tailed time series

03/30/2023
by   Muneya Matsui, et al.
0

We study the joint limit behavior of sums, maxima and ℓ^p-type moduli for samples taken from an ℝ^d-valued regularly varying stationary sequence with infinite variance. As a consequence, we can determine the distributional limits for ratios of sums and maxima, studentized sums, and other self-normalized quantities in terms of hybrid characteristic functions and Laplace transforms. These transforms enable one to calculate moments of the limits and to characterize the differences between the iid and stationary cases in terms of indices which describe effects of extremal clustering on functionals acting on the dependent sequence.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/13/2019

Characteristic Power Series of Graph Limits

In this note, we show how to obtain a "characteristic power series" of g...
research
12/21/2022

Inference for Non-Stationary Heavy Tailed Time Series

We consider the problem of inference for non-stationary time series with...
research
04/29/2018

Statistical inference for heavy tailed series with extremal independence

We consider stationary time series {X_j, j ∈ Z} whose finite dimensional...
research
01/14/2020

Large sample autocovariance matrices of linear processes with heavy tails

We provide asymptotic theory for certain functions of the sample autocov...
research
05/30/2019

A note on quadratic forms of stationary functional time series under mild conditions

We study the distributional properties of a quadratic form of a stationa...
research
10/09/2021

Tails of bivariate stochastic recurrence equation with triangular matrices

We study bivariate stochastic recurrence equations with triangular matri...
research
05/14/2018

Fork and Join Queueing Networks with Heavy Tails: Scaling Dimension and Throughput Limit

Parallel and distributed computing systems are foundational to the succe...

Please sign up or login with your details

Forgot password? Click here to reset