Large sample inference for long memory processes
 Responsibility
 Liudas Giraitis, Hira L. Koul, Donatas Surgailis.
 Language
 English.
 Imprint
 London : Imperial College Press ; Hackensack, NJ : Distributed by World Scientific Pub., c2012.
 Physical description
 xvi, 577 p. : ill ; 24 cm.
Access
Available online
Math & Statistics Library
Stacks
Call number  Status 

QA276 .G49 2012  Unknown 
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Creators/Contributors
 Author/Creator
 Giraitis, Liudas.
 Contributor
 Koul, H. L. (Hira L.)
 Surgailis, Donatas.
Contents/Summary
 Bibliography
 Includes bibliographical references (p. 554570) and indexes.
 Contents

 Introduction Estimation Some Inference Problems Residual Empirical Processes Regression Models Nonparametric Regression with Heteroscedastic Errors Model Checking under Long Memory Long Memory under Infinite Variance.
 (source: Nielsen Book Data)
 Publisher's Summary
 A discretetime stationary stochastic process with finite variance is said to have long memory if its autocorrelations tend to zero hyperbolically in the lag, i.e. like a power of the lag, as the lag tends to infinity. The absolute sum of autocorrelations of such processes diverges and their spectral density at the origin is unbounded. This is unlike the socalled weakly dependent processes, where autocorrelations tend to zero exponentially fast and the spectral density is bounded at the origin. In a long memory process, the dependence between the current observation and the one at a distant future is persistent; whereas in the weakly dependent processes, these observations are approximately independent. This fact alone is enough to warn a person about the validity of the classical inference procedures based on the square root of the sample size standardization when data are generated by a longterm memory process. The aim of this volume is to provide a text at the graduate level from which one can learn, in a concise fashion, some basic theory and techniques of proving limit theorems for numerous statistics based on long memory processes. It also provides a guide to researchers about some of the inference problems under long memory.
(source: Nielsen Book Data)
Subjects
 Subject
 Mathematical statistics.
Bibliographic information
 Publication date
 2012
 ISBN
 9781848162785 (hbk.)
 1848162782 (hbk.)