الصفحة 2
الصفحة 2
Modern Portfolio Optimization with NuOPT™, S-PLUS®, and S+Bayes™
This book fills the gap between current university instruction and current industry practice by providing a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods. The computational aspect of the book is based on extensive use of S-Plus®, the S+NuOPT™ optimization module, the S-Plus Robust Library and the S+Bayes™ Library, along with about 100 S-Plus scripts and some CRSP® sample data sets of stock returns. A special time-limited version of the S-Plus software is available to purchasers of this book.
Modern Multivariate Statistical Techniques : Regression, Classification, and Manifold Learning
Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods.
Modern Multidimensional Scalin : Theory and Applications
The book provides a comprehensive treatment of multidimensional scaling (MDS), a family of statistical techniques for analyzing the structure of (dis)similarity data. This book may be used as an introduction to MDS for students in psychology, sociology, and marketing. The prerequisite is an elementary background in statistics. The book is also well suited for a variety of advanced courses on MDS topics. All the mathematics required for more advanced topics is developed systematically. This second edition adds some 140 pages of new material. Many chapters are revised or have sections reflecting new insights and developments in MDS. There are two new chapters, one on asymmetric models and the other on unfolding. There are also numerous exercises that help the reader to practice what he or she has learned, and to delve deeper into the models and its intricacies.
Modern Mathematical Statistics with Applications
This book tries to strike a balance between mathematical foundations and statistical practice. The book provides a clear and current exposition of statistical concepts and methodology, including many examples and exercises based on real data gleaned from publicly available sources. The main focus of the book is on presenting and illustrating methods of inferential statistics used by investigators in a wide variety of disciplines, from actuarial science all the way to zoology. It begins with a chapter on descriptive statistics that immediately exposes the reader to the analysis of real data. The next six chapters develop the probability material that facilitates the transition from simply describing data to drawing formal conclusions based on inferential methodology. Point estimation, the use of statistical intervals, and hypothesis testing are the topics of the first three inferential chapters. The remainder of the book explores the use of these methods in a variety of more complex settings.
Modern Hematology : Biology and Clinical Management
The first chapters of this book contain a self-contained introduction to path integrals in Euclidean quantum mechanics and statistical mechanics. The resulting high-dimensional integrals can be estimated with the help of Monte Carlo simulations based on Markov processes. The most commonly used algorithms are presented in detail so as to prepare the reader for the use of high-performance computers as an “experimental” tool for this burgeoning field of theoretical physics. Several chapters are then devoted to an introduction to simple lattice field theories and a variety of spin systems with discrete and continuous spins, where the ubiquitous Ising model serves as an ideal guide for introducing the fascinating area of phase transitions. As an alternative to the lattice formulation of quantum field theories, variants of the flexible renormalization group methods are discussed in detail. Since, according to our present-day knowledge, all fundamental interactions in nature are described by gauge theories, the remaining chapters of the book deal with gauge theories without and with matter.
Modern Econometric Analysis : Surveys on Recent Developments
The importance of empirical economics and econometric methods has greatly in creased during the last 20 years due to the availability of better data and the improved performance of computers. In an information-driven society such as ours we need quickly to obtain complete and convincing statistical results. This is only possible if the appropriate econometric methods are applied. Traditional econometric analysis concentrates on classical methods which are far from suitable for handling actual economic problems.
Modern cryptography ; Vol.1 : A classical introduction to informational and mathematical principle
This book systematically explores the statistical characteristics of cryptographic systems, the computational complexity theory of cryptographic algorithms and the mathematical principles behind various encryption and decryption algorithms. The theory stems from technology. Based on Shannon's information theory, this book systematically introduces the information theory, statistical characteristics and computational complexity theory of public key cryptography, focusing on the three main algorithms of public key cryptography, RSA, discrete logarithm and elliptic curve cryptosystem.
Modelling Critical and Catastrophic Phenomena in Geoscience : A Statistical Physics Approach
This book presents a broad survey of models for critical and catastrophic phenomena in the geosciences, with strong emphasis on earthquakes. It assumes the perspective of statistical physics, which provides the theoretical frame for dealing with complex systems in general. This volume addresses graduate students wishing to specialize in the field and researchers working or interested in the field having a background in the physics, geosciences or applied mathematics.
Modelli Dinamici Discreti = Discrete Dynamic Models
Discrete mathematical modeling is one of the driving factors in modern mathematics research, and has played a role of synthesis between different disciplines, becoming a tool for qualitative and quantitative analysis in applied sciences. This volume provides an introduction to the analysis of discrete dynamic systems, following a modeling approach. An examination of a wide range of examples, models, and motivations drawn from Biology, Demography, Engineering and Economics, is followed by the presentation of the tools for the study of linear and non-linear scalar dynamical systems, with particular attention to stability analysis. The linear difference equations are studied in detail and an elementary introduction to the Z and DFT transforms is provided. One chapter is devoted to the study of bifurcations and chaotic dynamics. One-step vector dynamical systems and the applications of Markov chains are the subject of three chapters.
Modélisation et statistique spatiales = Spatial modeling and statistics
Spatial statistics are undergoing significant development due to their use in many fields: earth sciences, environment and climatology, epidemiology, econometrics, image analysis, etc. This book presents the main spatial models used as well as their statistics for the three types of data: geostatistics (observation on a continuous domain), data on a discrete network, point data. The objective is to present in a concise but mathematically complete way the most classical models (second order and variogram; software model and Gibbs-Markov field; point processes) as well as their simulation by MCMC algorithm. Then comes the presentation of statistical tools useful for their study.
Modeling Uncertainty : An Examination of Stochastic Theory, Methods, and Applications
Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internationally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There are papers with a theoretical emphasis and others that focus on applications. A number of papers survey the work in a particular area and in a few papers the authors present their personal view of a topic. It is a book with a considerable number of expository articles, which are accessible to a nonexpert - a graduate student in mathematics, statistics, engineering, and economics departments, or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic. Many of the papers present the state of the art of a specific area or represent original contributions which advance the present state of knowledge. In sum.
Modeling Longitudinal Data
This book teaches the art and statistical science of modern longitudinal data analysis. The author emphasizes specifying, understanding, and interpreting longitudinal data models. He inspects the longitudinal data graphically, analyzes the time trend and covariates, models the covariance matrix, and then draws conclusions. The book has many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material.
Modeling Financial Time Series with S-PLUS®
This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. This is the first book to show the power of S-PLUS for the analysis of time series data.It covers S+FinMetrics 2.0 and includes new chapters on copulas, nonlinear regime switching models, continuous-time financial models, generalized method of moments, semi-nonparametric conditional density models, and the efficient method of moments.
Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis
DEA is computational at its core and this book by Zhu and Cook deals with the micro aspects of handling and modeling data issues in modeling DEA problems. DEA's use has grown with its capability of dealing with complex "service industry" and the "public service domain" types of problems that require modeling both qualitative and quantitative data. It is a handbook treatment dealing with specific data problems including the following: (1) imprecise data, (2) inaccurate data, (3) missing data, (4) qualitative data, (5) outliers, (6) undesirable outputs, (7) quality data, (8) statistical analysis, (9) software and other data aspects of modeling complex DEA problems. In addition, the book demonstrates how to visualize DEA results when the data is more than 3-dimensional, and how to identify efficiency units quickly and accurately.
Modeling Chemical Systems using Cellular Automata
The book will be of great value in undergraduate courses in chemistry, physics, biology, applied mathematics, and bioinformatics, and as a supplement for laboratory courses in introductory chemistry, organic chemistry, physical chemistry, medicinal chemistry, chemical engineering and other courses dealing with statistical and dynamic systems. It allows the exploration of a wide range of dynamic phenomena, many of which are not normally accessible within conventional laboratory settings due to limitations of time, cost, and experimental equipment. The book is both a textbook on applied Cellular Automata and a lab manual for chemistry (physics, engineering) courses with lab activity. It would supplement other lab work and be an additonal book the students would use in the course.
Model-based Geostatistics
Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomena, using data obtained at a limited number of spatial locations. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume is the first book-length treatment of model-based geostatistics. The authors have written an expository text, emphasizing statistical methods and applications rather than the underlying mathematical theory. Analyses of datasets from a range of scientific contexts feature prominently, and simulations are used to illustrate theoretical results. Readers can reproduce most of the computational results in the book by using the authors' R-based software package, geoR, whose usage is illustrated in a computation section at the end of each chapter.
Model Reduction and Coarse-Graining Approaches for Multiscale Phenomena
Model reduction and coarse-graining are important in many areas of science and engineering. How does a system with many degrees of freedom become one with fewer? How can a reversible micro-description be adapted to the dissipative macroscopic model? These crucial questions, as well as many other related problems, are discussed in this book. Specific areas of study include dynamical systems, non-equilibrium statistical mechanics, kinetic theory, hydrodynamics and mechanics of continuous media, (bio)chemical kinetics, nonlinear dynamics, nonlinear control, nonlinear estimation, and particulate systems from various branches of engineering. The generic nature and the power of the pertinent conceptual, analytical and computational frameworks helps eliminate some of the traditional language barriers, which often unnecessarily impede scientific progress and the interaction of researchers between disciplines such as physics, chemistry, biology, applied mathematics and engineering. All contributions are authored by experts, whose specialities span a wide range of fields within science and engineering.
Model Based Inference in the Life Sciences : A Primer on Evidence
The abstract concept of "information" can be quantified and this has led to many important advances in the analysis of data in the empirical sciences. This text focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The fundamental science question relates to the empirical evidence for hypotheses in this set—a formal strength of evidence. Kullback-Leibler information is the information lost when a model is used to approximate full reality. Hirotugu Akaike found a link between K-L information (a cornerstone of information theory) and the maximized log-likelihood (a cornerstone of mathematical statistics). This combination has become the basis for a new paradigm in model based inference. The text advocates formal inference from all the hypotheses/models in the a priori set—multimodel inference.
mODa 8 - Advances in Model-Oriented Design and Analysis ; Proceedings of the 8th International Workshop in Model-Oriented Design and Analysis held in Almagro, Spain, June 4–8, 2007
The volume contains the proceedings of the 8th Workshop on Model-Oriented Design and Analysis. This book offers leading and pioneering work on optimal experimental designs, both from a mathematical/statistical point of view and with regard to real applications. Scientists from all over the world, from Eastern and Western Europe, the USA, Latin-America, Asia and Africa, have contributed to this volume. Primary topics are designs for nonlinear models and applications to experimental medicine.
Microsimulation Population Projections with SAS : A Reference Guide
This book for a researcher needs to perform microsimulation for population projections, building its own model with a common statistical software such as SAS might a good option, because this software is widely used among scholars and is taught in most social sciences departments. We define what is microsimulation: a modelling based on individual-level data rather than aggregated level data, in which transitions between the states are determined stochastically with a random experiment. We finally provide some examples of microsimulation models used by social scientists.
