الصفحة 1
الصفحة 1
Nonparametric Functional Data Analysis : Theory and Practice
Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas. This book starts from theoretical foundations including functional nonparametric modeling, description of the mathematical framework, construction of the statistical methods, and statements of their asymptotic behaviors. It proceeds to computational issues including R and S-PLUS routines. Several functional datasets in chemometrics, econometrics, and pattern recognition are used to emphasize the wide scope of nonparametric functional data analysis in applied sciences. The companion Web site includes R and S-PLUS routines, command lines for reproducing examples presented in the book, and the functional datasets. Rather than set application against theory, this book is really an interface of these two features of statistics. A special effort has been made in writing this book to accommodate several levels of reading.
Multilevel Modelling for Public Health and Health Services Research : Health in Context
This book is a practical introduction to multilevel modelling or multilevel analysis (MLA) – a statistical technique being increasingly used in public health and health services research.
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.
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.
Information Processing with Evolutionary Algorithms : From Industrial Applications to Academic Speculations
The last decade of the 20th century has witnessed a surge of interest in num- ical, computation-intensive approaches to information processing. The lines that draw the boundaries among statistics, optimization, arti cial intelligence and information processing are disappearing, and it is not uncommon to nd well-founded and sophisticated mathematical approaches in application - mains traditionally associated with ad-hoc programming. Heuristics has - come a branch of optimization and statistics. Clustering is applied to analyze soft data and to provide fast indexing in the World Wide Web. Non-trivial matrix algebra is at the heart of the last advances in computer vision. The breakthrough impulse was, apparently, due to the rise of the interest in arti cial neural networks, after its rediscovery in the late 1980s. Disguised as ANN, numerical and statistical methods made an appearance in the - formation processing scene, and others followed. A key component in many intelligent computational processing is the search for an optimal value of some function. Sometimes, this function is not evident and it must be made explicit in order to formulate the problem as an optimization problem. The search - ten takes place in high-dimensional spaces that can be either discrete, or c- tinuous or mixed. The shape of the high-dimensional surface that corresponds to the optimized function is usually very complex. Evolutionary algorithms are increasingly being applied to information processing applications that require any kind of optimization.
Growth Dynamics of Conifer Tree Rings : Images of Past and Future Environments
Each tree ring contains an image of the time when the ring formed, projected onto the ring's size, structure, and composition. Tree rings thus are natural archives of past environments, and contain records of past climate. While dendrochronologists have investigated the impact of climate on tree-ring growth by empirical–statistical methods, this volume presents a process-based model complementing previous approaches. Basic ideas concerning the biology of tree-ring growth and its control by environmental factors are treated, especially for conifers. The use of the model is illustrated by means of several examples from widely differing environments, and possible future directions for model development and application are discussed. The volume provides an improved mechanistic basis for the interpretation of tree rings as records of past climate. It advances process understanding of the large-scale environmental control of wood growth. As forests are the main carbon sink on land, the results are of great importance for all global change studies.
Fuzzy logic with engineering applications
With numerous examples and end-of-chapter problems, this book is essential reading for graduates and senior undergraduate students in civil, chemical, mechanical and electrical engineering as wells as researchers and practitioners working with fuzzy logic in industry.
Frontiers in Statistical Quality Control 8
The proportion of nonconforming meters in a lot has traditionally defined lot quality for utility meter sampling inspection purposes. However, lot quality is usually measured on the basis of two criteria for such products: the proportion of nonc- forming packages in the lot and the lot mean quantity.
From microphysics to macrophysics : Methods and applications of statistical physics ; Vol.2
Volume 2 applies statistical methods to systems governed by quantum effects, in particular to solid state physics, explaining properties due to the crystal structure or to the lattice excitations or to the electrons. Liquid helium is discussed and radiative equilibrium and transport are studied. The last chapters are devoted to non-equilibrium processes and to kinetic equations, with many applications included.
Forest Mensuration
Van Laar and Akça’s popular text book, Forest Mensuration, was first published in 1997. Like that first edition, this modern update is based on extensive research, teaching and practical experience in both Europe, and the tropics and subtropics. However, it has also been extensively revised, and now includes chapters on remote sensing and the application of aerial photographs and satellite imagery. As with its predecessor, this book assumes no advanced knowledge of statistical methods, and combines practical techniques with important historical and disciplinary context. The result is a strong balance between a handbook on traditional mensuration methods, and a valuable reference on the many recent research and inventory-related innovations which have emerged in recent years.
Deterministic and statistical methods in Machine Learning ; 1st International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures
This book consitutes the refereed proceedings of the First International Workshop on Machine Learning held in Sheffield, UK, in September 2004. The 19 revised full papers presented were carefully reviewed and selected for inclusion in the book. They address all current issues in the rapidly maturing field of machine learning that aims to provide practical methods for data discovery, categorisation and modelling. The particular focus of the workshop was advanced research methods in machine learning and statistical signal processing.
Design and Analysis of Simulation Experiments
This is an advanced expository book on statistical methods for the Design and Analysis of Simulation Experiments (DASE). Though the book focuses on DASE for discrete-event simulation (such as queuing and inventory simulations), it also discusses DASE for deterministic simulation (such as engineering and physics simulations). The text presents both classic and modern statistical designs. Classic designs (e.g., fractional factorials) assume only a few factors with a few values per factor. The resulting input/output data of the simulation experiment are analyzed through low-order polynomials, which are linear regression (meta)models. Modern designs allow many more factors, possible with many values per factor. These designs include group screening (e.g., Sequential Bifurcation, SB) and space filling designs (e.g., Latin Hypercube Sampling, LHS). The data resulting from these modern designs may be analyzed through low-order polynomials for group screening and various metamodel types (e.g., Kriging) for LHS.
Dental statistics made easy ; 3rd ed.
Presents the basics of dental statistics in an accessible way, combining explanation in non-technical language with key messages, practical examples, suggestions for further reading and exercises complete with detailed solutions. There is an emphasis on the principles and application of statistics without the use of algebra.
Deep Data Analytics for New Product Development
The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole.
Data visualization and analysis in second language research
This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages.
Data Monitoring in Clinical Trials : A Case Studies Approach
Randomized clinical trials are the gold standard for establishing many clinical practice guidelines and are central to evidence based medicine. Obtaining the best evidence through clinical trials must be done within the boundaries of rigorous science and ethical principles. One fundamental principle is that trials should not continue longer than necessary to reach their objectives. Therefore, trials must be monitored for recruitment progress, quality of data, adherence to patient care or prevention standards, and early evidence of benefit or harm. Frequently, a group of external experts, independent from the investigators and trial sponsor, is charged with this monitoring responsibility, especially for safety and early benefit. This group is referred to by various names, such as a data monitoring committee or a data and safety monitoring board. This book, through a series of case studies presented by many distinguished clinical trial experts, illustrates the complexity of this monitoring process.No other text has as extensive a collection of cases which provide insight into the many issues, often conflicting, that must be examined before recommendations to continue or discontinue a trial can be made. While depth in statistical methods is not required, some familiarity with statistical design and analysis issues in clinical trials is helpful. The cases cover trials which were terminated early for convincing evidence of benefit, or for harmful effects. Cases with complex issues are also included. This series of cases should provide broad background information for potential monitoring committee members and better prepare them for the challenges that may exist in the trials for which they are responsible.
Mathematical Modeling for the Life Sciences
Proposing a wide range of mathematical models that are currently used in life sciences may be regarded as a challenge, and that is precisely the challenge that this book takes up. Of course this panoramic study does not claim to offer a detailed and exhaustive view of the many interactions between mathematical models and life sciences. This textbook provides a general overview of realistic mathematical models in life sciences, considering both deterministic and stochastic models and covering dynamical systems, game theory, stochastic processes and statistical methods. Each mathematical model is explained and illustrated individually with an appropriate biological example. Finally three appendices on ordinary differential equations, evolution equations, and probability are added to make it possible to read this book independently of other literature.
Mathematical and Statistical Methods in Insurance and Finance
The interaction between mathematicians and statisticians reveals to be an effective approach to the analysis of insurance and financial problems, in particular in an operative perspective. The Maf2006 conference, held at the University of Salerno in 2006, had precisely this purpose and the collection here published gathers some of the papers presented at the conference and successively worked out to this aim. They cover a wide variety of subjects in insurance and financial fields, all treated in light of the successful cooperation between the two quantitative methods.
Classical Methods of Statistics : With Applications in Fusion-Oriented Plasma Physics
Classical Methods of Statistics is a blend of theory and practical statistical methods written for graduate students and researchers interested in applications to plasma physics and its experimental aspects. It can also fruitfully be used by students majoring in probability theory and statistics. In the first part, the mathematical framework and some of the history of the subject are described. Many exercises help readers to understand the underlying concepts. In the second part, two case studies are presented exemplifying discriminant analysis and multivariate profile analysis. The introductions of these case studies outline contextual magnetic plasma fusion research. In the third part, an overview of statistical software is given and, in particular, SAS and S-PLUS are discussed. In the last chapter, several datasets with guided exercises, predominantly from the ASDEX Upgrade tokamak, are included and their physical background is concisely described. The book concludes with a list of essential keyword translations.
Carbonate Reservoir Characterization : An Integrated Approach
One principal need in petroleum recovery from carbonate reservoirs is the description of the three-dimensional distribution of petrophysical properties in order to improve performance predictions by means of fluid-flow computer simulations. The book focuses on a rock based approach for the integration of geological, petrophysical, and geostatistical methods to construct a reservoir model suitable to input into flow simulation programs. This second edition includes a new chapter on model construction and new examples of limestone, dolostone, and touching-vug reservoir models as well as improved chapters on basic petrophysical properties, rock-fabric/petrophysical relationships, calibration of wireline logs, and sequence stratigraphy.
