This book presents recent findings on central and non-central limit theorems for Toeplitz and tapered Toeplitz random quadratic functionals of stationary processes, with applications in spectral-based statistical inference. It focuses on Gaussian, orthogonal increment-driven, and Lévy-driven linear stationary processes with memory, in both discrete and continuous time.

Toeplitz matrices and operators are central to the study of stationary processes. The covariance matrix of a discrete-time stationary process is a truncated Toeplitz matrix generated by the process's spectral density; in continuous-time, this becomes a Toeplitz operator. The foundations of the trace approximation problem were laid by Grenander and Szegö in their classical monograph Toeplitz Forms and Their Applications (1958), and the subject has recently seen renewed interest due to developments in long-range dependence and tapered data analysis.

The book addresses topics that are often overlooked in other texts, including the trace approximation problem, central limit theorems in continuous time, functional central and non-central limit theorems for Toeplitz processes, and central limit theorems for tapered functionals. It also covers approaches to estimating linear and nonlinear spectral functionals, Whittle estimators, and goodness-of-fit tests using tapered data each enriched by new advances in the field.

Comprising ten chapters and two appendices, the book begins with an overview of the main problems and a review of foundational concepts from real analysis, functional analysis, and matrix analysis. It then introduces a model that is a second-order stationary process and discusses key concepts and results from the general theory of stationary processes, before delving into the trace approximation problem. Subsequent chapters cover central and non-central limit theorems for Toeplitz and tapered Toeplitz random quadratic functionals and explore statistical inference problems. The appendices discuss the motivations and benefits of data tapering, and outline several important problems closely related to the main themes of the book.

The text will be a valuable resource for researchers in time series analysis, econometrics, finance, and applied statistics. It is suitable for graduate-level courses in time series analysis or the statistics of stochastic processes, and as a supplementary reference for students of advanced statistics, probability, econometrics, or finance.

Discusses limit theorems for Toeplitz and tapered Toeplitz-type random quadratic functionals of stationary processes Addresses statistical inference problems related to the spectra of the observed processes Presents a unified treatment of the trace approximation problem for Toeplitz matrices and operators

Autorentext

Mamikon S. Ginovyan has been a Professor of Mathematics and Statistics at Boston University since 2004. Before joining Boston University, he served as a Senior Researcher and Deputy Research Director at the Institute of Mathematics of the Armenian Academy of Sciences. He earned both his B.S. and M.S. degrees in Mathematics from Yerevan State University in Armenia and completed his Ph.D. in Mathematics at the St. Petersburg Department of the Steklov Mathematical Institute of the Russian Academy of Sciences. Dr. Ginovyan's research interests include probability and statistics, as well as related areas in harmonic analysis and operator theory. His published work covers a wide range of topics, including limit theorems, estimation and prediction in stationary stochastic models, and problems related to Toeplitz operators and matrices.

Inhalt

• 1. Introduction and Overview.- 2. Preliminaries.- 3. The Model: Second-Order (Weakly) Stationary Processes.- 4. The Trace Problem for Toeplitz Matrices and Operators.- 5. Central Limit Theorems for Random Toeplitz Quadratic Functionals.- 6. Functional Limit Theorems for Toeplitz Processes.- 7. Central Limit Theorems for Tapered Toeplitz Quadratic Functionals.- 8. Nonparametric Estimation of Spectral Functionals.- 9. Parametric Estimation. The Whittle Estimation Procedure.- 10. Goodness-of-Fit Tests Based on the Tapered Data.