Bøger i Lecture Notes in Statistics serien

1.1 Rationale.- 1.2 An Overview.- Soil Acidification: Fundamental Concepts.- The Sulfur System.- 3.1 The Sulfate Cycle.- 3.2 Sulfate Adsorption.- 3.3 The Biotic Component.- 3.4 Summary.- The Nitrogen System.- 4.1 Acid-Base Relationships of the Nitrogen Cycle.- 4.2 Acid-Base Relationships of Nitrogen Inputs.- 4.3 Ecosystem Effects.- Soil-Solution Interactions.- 5.1 Role of Anions.- 5.2 Ion Equilibria Model.- 5.3 Conceptualizing the Model.- 5.4 Solution Concentration Effects.- 5.5 Cation Removal, ?M/?H.- 5.6 Complexes and Precipitates.- 5.7 Organic Anions.- 5.8 Summary.- Forest Element Cycling.- 6.1 Definition of Terms.- 6.2 Effects of Acid Deposition on Base Cation Flux.- 6.3 Cation Nutrient Effects.- The Aquatic Interface.- 7.1 Alkalinity Concepts.- 7.2 Alkalinity in Soil Solution.- 7.3 Naturally Acid Waters.- 7.4 Sensitivity to Water Acidification.- 7.5 Capacity versus Intensity.- Soil Sensitivity.- Base Cation Depletion.- Sensitivity to pH Changes.- Sensitivity to Aluminum Mobilization.- Summary.- Conclusion.- 9.1 Summary.- 9.2 Concepts in Transition.- References.- Appendix: Model Documentation.- A.1 Model Description.- A.2 Program Operation.- A.3 Program Documentation.- A.4 Program Listings.

This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.