Empirical Process Techniques for Dependent Data

• I. A Tutorial on Empirical Process Techniques for Dependent Data.- Empirical Process Techniques for Dependent Data.- II. Techniques for the Empirical Process of Stationary Sequences.- Weak Dependence: Models and Applications.- Maximal Inequalities and Empirical Central Limit Theorems.- On Hoeffding’s Inequality for Dependent Random Variables.- On the Coupling of Dependent Random Variables and Applications.- Empirical Processes of Residuals.- III. The Empirical Process of Long Range Dependent Processes.- Asymptotic Expansion of the Empirical Process of Long Memory Moving Averages.- The Reduction Principle for the Empirical Process of a Long Memory Linear Process.- Distributional Limit Theorems for Empirical Processes Generated by Functions of a Stationary Gaussian Process.- IV. Empirical Spectral Process Techniques.- Empirical Spectral Processes and Nonparametric Maximum Likelihood Estimation for Time Series.- Empirical Processes Techniques for the Spectral Estimation of Fractional Processes.- V. The Tail Empirical Process in Extreme Value Theory.- Tail Empirical Processes Under Mixing Conditions.- VI. Bootstrap Techniques.- On the Bootstrap and Empirical Processes for Dependent Sequences.- Frequency Domain Bootstrap for Time Series.

"The book is an outgrowth of the workshop held in November 2000 at the University of Copenhagen. It opens by an extensive tutorial covering the topic from the early roots up to recent developments and is accompanied by a vast bibliography of newly 150 items... The book is the first comprehensive treatment of this topic, perhaps because only the present-day computers are able to meet the enormous requirements for high speed and large memory necessary for the application of statistical techniques to dependent data. It will be suitable for classroom use as well as for specialists in probability and statistics and for practitioners in the above mentioned branches of dependent data applications." ---APPLICATIONS OF MATHEMATICS