Open Access
2014 The trace problem for Toeplitz matrices and operators and its impact in probability
Mamikon S. Ginovyan, Artur A. Sahakyan, Murad S. Taqqu
Probab. Surveys 11: 393-440 (2014). DOI: 10.1214/13-PS217


The trace approximation problem for Toeplitz matrices and its applications to stationary processes dates back to the classic book by Grenander and Szegö, Toeplitz forms and their applications (University of California Press, Berkeley, 1958). It has then been extensively studied in the literature.

In this paper we provide a survey and unified treatment of the trace approximation problem both for Toeplitz matrices and for operators and describe applications to discrete- and continuous-time stationary processes.

The trace approximation problem serves indeed as a tool to study many probabilistic and statistical topics for stationary models. These include central and non-central limit theorems and large deviations of Toeplitz type random quadratic functionals, parametric and nonparametric estimation, prediction of the future value based on the observed past of the process, hypotheses testing about the spectrum, etc.

We review and summarize the known results concerning the trace approximation problem, prove some new results, and provide a number of applications to discrete- and continuous-time stationary time series models with various types of memory structures, such as long memory, anti-persistent and short memory.


Download Citation

Mamikon S. Ginovyan. Artur A. Sahakyan. Murad S. Taqqu. "The trace problem for Toeplitz matrices and operators and its impact in probability." Probab. Surveys 11 393 - 440, 2014.


Published: 2014
First available in Project Euclid: 2 December 2014

zbMATH: 1348.60054
MathSciNet: MR3290440
Digital Object Identifier: 10.1214/13-PS217

Primary: 60G10 , 62G20
Secondary: 15B05 , 47B35

Keywords: central limit theorem , long-memory , singularity , Spectral density , stationary process , Toeplitz operator , trace approximation

Rights: Copyright © 2014 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.11 • 2014
Back to Top