Asymptotic Theory of Statistical Inference for Time Series
There has been much demand for the statistical analysis of dependent ob servations in many fields, for example, economics, engineering and the nat ural sciences. A model that describes the probability structure of a se ries of dependent observations is called a stochastic process. The primary aim...
Main Authors:  , 

Format:  eBook 
Language:  English 
Published: 
New York, NY
Springer New York
2000, 2000

Edition:  1st ed. 2000 
Series:  Springer Series in Statistics

Subjects:  
Online Access:  
Collection:  Springer Book Archives 2004  Collection details see MPG.ReNa 
Table of Contents:
 4.6 Higher Order Asymptotic Theory for Normalizing Transformations
 4.7 Generalization of LeCam’s Third Lemma and Higher Order Asymptotics of Iterative Methods
 Problems
 5 Asymptotic Theory for LongMemory Processes
 5.1 Some Elements of LongMemory Processes
 5.2 Limit Theorems for Fundamental Statistics
 5.3 Estimation and Testing Theory for LongMemory Processes
 5.4 Regression Models with LongMemory Disturbances
 5.5 Semiparametric Analysis and the LAN Approach
 Problems
 6 Statistical Analysis Based on Functionals of Spectra
 6.1 Estimation of Nonlinear Functionals of Spectra
 6.2 Application to Parameter Estimation for Stationary Processes
 6.3 Asymptotically Efficient Nonparametric Estimation of Functionals of Spectra in Gaussian Stationary Processes
 6.4 Robustness in the Frequency Domain Approach
 6.5 NumericalExamples
 Problems
 7 Discriminant Analysis for Stationary Time Series
 7.1 Basic Formulation
 7.2 Standard Methods for Gaussian Stationary Processes
 7.3 Discriminant Analysis for NonGaussian Linear Processes
 7.4 Nonparametric Approach for Discriminant Analysis
 7.5 Parametric Approach for Discriminant Analysis
 7.6 Derivation of Spectral Expressions to Divergence Measures Between Gaussian Stationary Processes
 7.7 Miscellany
 Problems
 8 Large Deviation Theory and Saddlepoint Approximation for Stochastic Processes
 8.1 Large Deviation Theorem 538 8.2 Asymptotic Efficiency for Gaussian Stationary Processes:Large Deviation Approach
 8.3 Large Deviation Results for an OrnsteinUhlenbeck Process
 8.4 Saddlepoint Approximations for Stochastic Processes
 Problems
 A.1 Mathematics
 A.2 Probability
 A.3 Statistics
 1 Elements of Stochastic Processes
 1.1 Introduction
 1.2 Stochastic Processes
 1.3 Limit Theorems
 Problems
 2 Local Asymptotic Normality for Stochastic Processes
 2.1 General Results for Local Asymptotic Normality
 2.2 Local Asymptotic Normality for Linear Processes
 Problems
 3 Asymptotic Theory of Estimation and Testing for Stochastic Processes
 3.1 Asymptotic Theory of Estimation and Testing for Linear Processes
 3.2 Asymptotic Theory for Nonlinear Stochastic Models
 3.3 Asymptotic Theory for Continuous Time Processes
 Problems
 4 Higher Order Asymptotic Theory for Stochastic Processes
 4.1 Introduction to Higher Order Asymptotic Theory
 4.2 Valid Asymptotic Expansions
 4.3 Higher Order Asymptotic Estimation Theory for Discrete Time Processes in View of Statistical Differential Geometry
 4.4 Higher Order Asymptotic Theory for Continuous Time Processes
 4.5 Higher Order Asymptotic Theory for Testing Problems