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Algorithms, Routines, and S-Functions for Robust Statistics
ROBETH (written in ANSI FORTRAN 77) is a systematized collection of algorithms that allows computation of a broad class of procedures based on M- and high-breakdown point estimation, including robust regression, robust testing of linear hypotheses, and robust coveriances. This book describes the computational procedures included in ROBETH. Each chapter is organized into three parts: 1. An overview of the theoretical background for the statistical and numerical methods 2. A detailed description of the corresponding FORTRAN subroutines and of the numerical algorithms as they are implemented 3. The scripts of several examples concerning the use of ROBETH by means of the S-PLUS interface, including some examples of high-level S functions.
Theory of Linear Models
Providing a self-contained exposition of the theory of linear models, this treatise strikes a compromise between theory and practice, providing a sound theoretical basis while putting the theory to work in important cases.
Bayesian Mediation Analysis using R
Delve into the realm of statistical methodology for mediation analysis with a Bayesian perspective in high dimensional data through this comprehensive guide. Focused on various forms of time-to-event data methodologies, this book helps readers master the application of Bayesian mediation analysis using R. Across ten chapters, this book explores concepts of mediation analysis, survival analysis, accelerated failure time modeling, longitudinal data analysis, and competing risk modeling. Each chapter progressively unravels intricate topics, from the foundations of Bayesian approaches to advanced techniques like variable selection, bivariate survival models, and Dirichlet process priors. With practical examples and step-by-step guidance, this book empowers readers to navigate the intricate landscape of high-dimensional data analysis, fostering a deep understanding of its applications and significance in diverse fields.
Natural Computing in Computational Finance
The chapters in this book illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. The eleven chapters were selected following a rigorous, peer-reviewed, selection process.
Robust Statistics, Data Analysis, and Computer Intensive Methods
To celebrate Peter Huber's 60th birthday in 1994, our university had invited for a festive occasion in the afternoon of Thursday, June 9. The invitation to honour this outstanding personality was followed by about fifty colleagues and former students from, mainly, allover the world. Others, who could not attend, sent their congratulations by mail and e-mail (P. Bickel:" ... It's hard to imagine that Peter turned 60 ... "). After a welcome address by Adalbert Kerber (dean), the following lectures were delivered. Volker Strassen (Konstanz): Almost Sure Primes and Cryptography -an Introduction Frank Hampel (Zurich): On the Philosophical Foundations of Statistics 1 Andreas Buja (Murray Hill): Pr...
Directions in Robust Statistics and Diagnostics
This IMA Volume in Mathematics and its Applications DIRECTIONS IN ROBUST STATISTICS AND DIAGNOSTICS is based on the proceedings of the first four weeks of the six week IMA 1989 summer program "Robustness, Diagnostics, Computing and Graphics in Statistics". An important objective of the organizers was to draw a broad set of statisticians working in robustness or diagnostics into collaboration on the challenging problems in these areas, particularly on the interface between them. We thank the organizers of the robustness and diagnostics program Noel Cressie, Thomas P. Hettmansperger, Peter J. Huber, R. Douglas Martin, and especially Werner Stahel and Sanford Weisberg who edited the proceedings...
Numerical Methods and Optimization in Finance
Computationally-intensive tools play an increasingly important role in financial decisions. Many financial problems-ranging from asset allocation to risk management and from option pricing to model calibration-can be efficiently handled using modern computational techniques. Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics. This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested empirically. This revised edition includes two new chapters, a self-contained tutorial on implementing and using heuristics, and an explanation of software used for testing portfolio-selection models. Postgraduate students, researchers in programs on quantitative and computational finance, and practitioners in banks and other financial companies can benefit from this second edition of Numerical Methods and Optimization in Finance.
Developments in Robust Statistics
Aspects of Robust Statistics are important in many areas. Based on the International Conference on Robust Statistics 2001 (ICORS 2001) in Vorau, Austria, this volume discusses future directions of the discipline, bringing together leading scientists, experienced researchers and practitioners, as well as younger researchers. The papers cover a multitude of different aspects of Robust Statistics. For instance, the fundamental problem of data summary (weights of evidence) is considered and its robustness properties are studied. Further theoretical subjects include e.g.: robust methods for skewness, time series, longitudinal data, multivariate methods, and tests. Some papers deal with computational aspects and algorithms. Finally, the aspects of application and programming tools complete the volume.
Bearing Meaning
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Modern Portfolio Optimization with NuOPTTM, S-PLUS®, and S+BayesTM
In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management. This trend will only accelerate in the coming years. This practical handbook fills the gap between current university instruction and current industry practice. It provides a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods using the powerful NUOPT for S-PLUS optimizer.
