Visar resultat för..."Springer-Verlag New York Inc."

The general purpose of this book is to show how broad classes of stochastic processes can be of considerable use in the statistical modelling and analysis of survival data, or more broadly, in event history analysis. The authors will show a number of connections between process theory and statistics, focusing on the intuitive aspects and playing down theoretical details. The book shall also be practical, directed to people who want to apply the methods, and with reference to computer programs.

Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms:Curation and delivery of biological metadata for use in statistical modeling and interpretationStatistical analysis of high-throughput data, including machine learning and visualizationModeling and visualization of graphs and networksThe developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document.Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

This groundbreaking text has been established as the market leader throughout the world. Profusely illustrated, Transmission Electron Microscopy: A Textbook for Materials Science provides the necessary instructions for successful hands-on application of this versatile materials characterization technique. For this first new edition in 12 years, many sections have been completely rewritten with all others revised and updated. The new edition also includes an extensive collection of questions for the student, providing approximately 800 self-assessment questions and over 400 questions that are suitable for homework assignment. Four-color illustrations throughout also enhance the new edition.Praise for the first edition:`The best textbook for this audience available.' – American Scientist`Ideally suited to the needs of a graduate level course. It is hard to imagine this book not fulfilling most of the requirements of a text for such a course.' – Microscope`This book is written in such a comprehensive manner that it is understandable to all people who are trained in physical science and it will be useful both for the expert as well as the student.' – Micron`The book answers nearly any question - be it instrumental, practical, or theoretical - either directly or with an appropriate reference...This book provides a basic, clear-cut presentation of how transmission electron microscopes should be used and of how this depends specifically on one's specific undergoing project.' – MRS Bulletin, May 1998`The only complete text now available which includes all the remarkable advances made in the field of TEM in the past 30-40 years....The authors can be proud of an enormous task, very well done.' – from the Foreword by Professor Gareth Thomas, University of California, Berkeley

1. In the lumbar spinal canal of the achondroplast there is decreased cross-sectional area. In addition the intervertebral foramina are narrow. These changes result in reduced area for the dural sac and exiting spinal nerves. 2. There is associated thoracolumbar kyphosis and a lumbosacral hyperlordosis. These sagittal plane changes result in increased tension on the dural sac and nerves. 3. With aging there is disc degeneration with disc space narrowing and osteophyte formation. In addition facet hypertrophy with osteophyte formation are common. These degenerative changes further reduce the size of an already compromised spinal canal and intervertebral foramina. The above understanding of the normal spinal and anatomy in the achondroplast, and the added effects of aging allow the surgeon to plan a logical treatment regimen for neurological problems in the achondroplast. Fig. 6. Cross-section at the di sc level of an achondroplast (A) and an age matched non-achondroplast (B). The cross-sectional area of the spinal canal is reduced in the achondroplast, with a reduction in the antero- posterior canal (C vs. C 1 ).There is facet hyper- trophy extending into the nerve root canal and into the spinal canal lateral recess (B vs. B 1 ). In ad- dition the nerve root canal is markedly reduced in-the achondroplast (D vs. D1 ). (Reprinted with permis- sion, as of Fig. 5). 225 REFERENCES 1. R. J. Eulert, Scoliosis and kyphosis in dwarfing conditions, Arch. Orthop.Traum. Surg.102:45 (1983).

The computation of invariants of algebraic number fields such as integral bases, discriminants, prime decompositions, ideal class groups, and unit groups is important both for its own sake and for its numerous applications, for example, to the solution of Diophantine equations. The practical com­ pletion of this task (sometimes known as the Dedekind program) has been one of the major achievements of computational number theory in the past ten years, thanks to the efforts of many people. Even though some practical problems still exist, one can consider the subject as solved in a satisfactory manner, and it is now routine to ask a specialized Computer Algebra Sys­ tem such as Kant/Kash, liDIA, Magma, or Pari/GP, to perform number field computations that would have been unfeasible only ten years ago. The (very numerous) algorithms used are essentially all described in A Course in Com­ putational Algebraic Number Theory, GTM 138, first published in 1993 (third corrected printing 1996), which is referred to here as [CohO]. That text also treats other subjects such as elliptic curves, factoring, and primality testing. Itis important and natural to generalize these algorithms. Several gener­ alizations can be considered, but the most important are certainly the gen­ eralizations to global function fields (finite extensions of the field of rational functions in one variable overa finite field) and to relative extensions ofnum­ ber fields. As in [CohO], in the present book we will consider number fields only and not deal at all with function fields.

This textbook is a completely revised, updated, and expanded English edition of the important Analyse fonctionnelle (1983). Uniquely, this book presents in a coherent, concise and unified way the main results from functional analysis together with the main results from the theory of partial differential equations (PDEs).

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

In an expanding 2nd edition of How Apollo Flew to the Moon, David Woods tells the exciting story of how the resulting Apollo flights were conducted by following a virtual flight to the Moon and its exploration of the surface.

In this text, the author constructs the mathematical apparatus of classical mechanics from the beginning, examining all the basic problems in dynamics, including the theory of oscillations, the theory of rigid body motion, and the Hamiltonian formalism.

This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

From the reviews of the second edition:"This book covers many interesting topics not usually covered in a present day undergraduate course, as well as certain basic topics such as the development of the calculus and the solution of polynomial equations.

This is the only book that gives a rigorous and comprehensive treatment with lots of examples, exercises, remarks on this particular level between the standard first undergraduate course and the first graduate course based on measure theory. There is no competitor to this book. The book can be used in classrooms as well as for self-study.

This fresh edition, substantially revised and augmented, provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics. The examples used, analyzed using Stata, can be applied to other areas.

Acutely aware of the need for rigor, the student is much better prepared to understand what constitutes a proper mathematical proof and how to write one.Fifteen years of classroom experience with the first edition of Understanding Analysis have solidified and refined the central narrative of the second edition.

The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations.

This book deals with several aspects of what is now called "explicit number theory." The central theme is the solution of Diophantine equations, i.e., equations or systems of polynomial equations which must be solved in integers, rational numbers or more generally in algebraic numbers. This theme, in particular, is the central motivation for the modern theory of arithmetic algebraic geometry. In this text, this is considered through three of its most basic aspects. The local aspect, global aspect, and the third aspect is the theory of zeta and L-functions. This last aspect can be considered as a unifying theme for the whole subject.

This textbook introduces the well-posedness theory for initial-value problems of nonlinear, dispersive partial differential equations, with special focus on two key models, the Korteweg–de Vries equation and the nonlinear Schrödinger equation. A concise and self-contained treatment of background material (the Fourier transform, interpolation theory, Sobolev spaces, and the linear Schrödinger equation) prepares the reader to understand the main topics covered: the initial-value problem for the nonlinear Schrödinger equation and the generalized Korteweg–de Vries equation, properties of their solutions, and a survey of general classes of nonlinear dispersive equations of physical and mathematical significance. Each chapter ends with an expert account of recent developments and open problems, as well as exercises. The final chapter gives a detailed exposition of local well-posedness for the nonlinear Schrödinger equation, taking the reader to the forefront of recent research.Thesecond edition of Introduction to Nonlinear Dispersive Equations builds upon the success of the first edition by the addition of updated material on the main topics, an expanded bibliography, and new exercises. Assuming only basic knowledge of complex analysis and integration theory, this book will enable graduate students and researchers to enter this actively developing field.

From April through December of 1945, ten of Nazi Germany's greatest nuclear physicists were detained by Allied military and intelligence services in a kind of gilded cage at Farm Hall, an English country manor near Cambridge. The physicists knew the Reich had failed to develop an atomic bomb, and they soon learned, from a BBC radio report on August 6, that the Allies had succeeded in their own efforts to create such a weapon. But what they did not know was that many of their meetings and private conversations were being monitored and recorded by British agents. This book contains the complete collection of transcripts that were made from these secret recordings, providing an unprecedented view of how the German scientists, including two Nobel Laureates, thought and spoke about their roles during the war.

This text gives a rigorous treatment of the foundations of calculus. In contrast to more traditional approaches, infinite sequences and series are placed at the forefront. The approach taken has not only the merit of simplicity, but students are well placed to understand and appreciate more sophisticated concepts in advanced mathematics. The authors mitigate potential difficulties in mastering the material by motivating definitions, results and proofs. Simple examples are provided to illustrate new material and exercises are included at the end of most sections. Noteworthy topics include: an extensive discussion of convergence tests for infinite series, Wallis’s formula and Stirling’s formula, proofs of the irrationality of π and e and a treatment of Newton’s method as a special instance of finding fixed points of iterated functions.

Food Powders is the first book that addresses key aspects of food powder technology. It is a comprehensive review on the characterization of ingredients, semi-processed and finished products when they are in a powdered form. It includes the description of different industrial unit operations such as drying, grinding, mixing, agglomeration, and encapsulation that supply food powders of different composition and microstructure. It also includes several analytical tools to characterize food powders by their particle size, size distribution, physical properties, and functionality. Written from an engineering perspective, the book is designed and developed as a useful reference for individuals in both the food industry and academia interested in an organized and updated review. It is a valuable addition to the food powder technology literature and promotes additional interest in advancing food powders research, development, education and implementation.

The Encyclopedia of Health Psychology provides a comprehensive overview of this rapidly growing field. With over 200 entries from the leading researchers, educators, and practitioners in health psychology, The Encyclopedia of Health Psychology provides the most current, extensive, and accessible single-volume treatment of the subject available. Teachers, practitioners, school nurses, healthcare providers, students, as well as expert and non-expert readers will appreciate its organization and clarity. Readers interested in the psychology of health issues throughout the lifespan will find its entries engaging and instructive, whether they deal with chronic conditions, mind-body connections, or the consequences of increased life expectancy. The Encyclopedia of Health Psychology will serve as a useful reference for practitioners, as a topical primer for students, as a comprehensive guide for the expert, and as an accessible introduction for the lay reader.

Grade inflation runs rampant at most colleges and universities, but faculty and administrators are seemingly unwilling to face the problem. This book explains why, exposing many of the misconceptions surrounding college grading. Based on historical research and the results of a yearlong, on-line course evaluation experiment conducted at Duke University during the 1998-1999 academic year, the effects of student grading on various educational processes, and their subsequent impact on student and faculty behavior, is examined. Principal conclusions of this investigation are that instructors' grading practices have a significant influence on end-of-course teaching evaluations, and that student expectations of grading practices play an important role in the courses that students decide to take. The latter effect has a serious impact on course enrollments in the natural sciences and mathematics, while the combination of both mean that faculty have an incentive to award high grades, and students have an incentive to choose courses with faculty who do. Grade inflation is the natural consequence of this incentive system.Material contained in this book is essential reading for anyone involved in efforts to reform our postsecondary educational system, or for those who simply wish to survive and prosper in it. Valen Johnson is a Professor of Biostatistics at the University of Michigan. Prior to accepting an appointment in Ann Arbor, he was a Professor of Statistics and Decision Sciences at Duke University, where data for this book was collected. He is a Fellow of the American Statistical Association.

This book discusses the analysis of gene expression profile data from DNA micorarray studies and the design of such studies. The book will address design and analysis issues for both of the major classes of DNA microarrays, the cDNA microarrays and the oligonucleotide arrays. DNA microarrays are a new technology that is revolutionizing biological and biomedical research. Most biologists are trying to analyze their own data using a variety of commercial and public domain software. This book will provide an authoritative review of the methods available and present it in a manner that is intelligible to biologists.

Spatial statistics and Markov Chain Monte Carlo (MCMC) techniques have each undergone major developments in the last decade. Also, these two areas are mutually reinforcing, because MCMC methods are often necessary for the practical implementation of spatial statistical inference, while new spatial stochastic models in turn motivate the development of improved MCMC algorithms. This volume shows how sophisticated spatial statistical and computational methods apply to a range of problems of increasing importance for applications in science and technology. It consists of four chapters: 1. Petros Dellaportas and Gareth O. Roberts give a tutorial on MCMC methods, the computational methodology which is essential for virtually all the complex spatial models to be considered in subsequent chapters. 2. Peter J. Diggle, Paulo J, Ribeiro Jr., and Ole F. Christensen introduce the reader to the model- based approach to geostatistics, i.e. the application of general statistical principles to the formulation of explicit stochastic models for geostatistical data, and to inference within a declared class of models. 3. Merrilee A. Hurn, Oddvar K. Husby, and H?vard Rue discuss various aspects of image analysis, ranging from low to high level tasks, and illustrated with different examples of applications. 4. Jesper Moller and Rasmus P. Waggepetersen collect recent theoretical advances in simulation-based inference for spatial point processes, and discuss some examples of applications. The volume introduces topics of current interest in spatial and computational statistics, which should be accessible to postgraduate students as well as to experienced statistical researchers. It is partly based on the course material for the "TMR and MaPhySto Summer School on Spatial Statistics and Computational Methods," held at Aalborg University, Denmark, August 19-22, 2001.

This book provides a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, including Bayesian inference. A unique feature is that it treats the area of nonparametric function estimation from such data in detail, gathering results on this topic to date in one accessible volume. Previous books on records have focused mainly on the probabilistic behavior of records, prediction of future records, and characterizations of the distributions of record values, addressing some inference methods only briefly. The main purpose of this book is to fill this void on general inference from record values. Statisticians, mathematicians, and engineers will find the book useful as a research reference and in learning about making inferences from record-breaking data. The book can also serve as part of a graduate- level statistics or mathematics course, complementing material on the probabilistic aspects of record values. For a basic understanding of the statistical concepts, a one-year graduate course in mathematical statistics provides sufficient background.For a detailed understanding of the convergence theory of the nonparametric function estimators, a course in measure theory or probability theory at the graduate level is useful. Sneh Gulati is Associate Professor of Statistics at Florida International University in Miami. She is currently an associate editor of the Journal of Statistical Computation and Simulation and has published several articles in statistics. Currently she serves as the president of the South Florida Chapter of the American Statistical Association and is also the chair of the Florida Commission of Hurricane Loss Projection Methodology. William J. Padgett is Professor of Statistics and was the founding Chair of the Department of Statistics at the University of South

This book introduces the notion of an E-semigroup, a generalization of the known concept of E_O-semigroup. These objects are families of endomorphisms of a von Neumann algebra satisfying certain natural algebraic and continuity conditions. This monograph will be of interest to graduate students and research mathematicians.

Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.

The special manifolds of interest in this book are the Stiefel manifold and the Grassmann manifold. Formally, the Stiefel manifold Vk,m is the space of k­ frames in the m-dimensional real Euclidean space Rm, represented by the set of m x k matrices X such that X' X = I , where Ik is the k x k identity matrix, k and the Grassmann manifold Gk,m-k is the space of k-planes (k-dimensional hyperplanes) in Rm. We see that the manifold Pk,m-k of m x m orthogonal projection matrices idempotent of rank k corresponds uniquely to Gk,m-k. This book is concerned with statistical analysis on the manifolds Vk,m and Pk,m-k as statistical sample spaces consisting of matrices. The discussion is carried out on the real spaces so that scalars, vectors, and matrices treated in this book are all real, unless explicitly stated otherwise. For the special case k = 1, the observations from V1,m and G1,m-l are regarded as directed vectors on a unit sphere and as undirected axes or lines, respectively. There exists a large literature of applications of directional statis­ tics and its statistical analysis, mostly occurring for m = 2 or 3 in practice, in the Earth (or Geological) Sciences, Astrophysics, Medicine, Biology, Meteo­ rology, Animal Behavior, and many other fields. Examples of observations on the general Grassmann manifold Gk,m-k arise in the signal processing of radar with m elements observing k targets.

The challenges of problems from international programming competitions are an effective way to improve your algorithmic and coding skills and understanding. This volume uses international programming competition-type problems to motivate the study of algorithms, programming, and other topics in computer science. The book includes more than 100 programming challenges, as well as the theory and key concepts necessary for approaching them. Problems are organized by topic, and supplemented by complete tutorial material. Readers gain a concrete understanding of both algorithmic techniques and advanced coding topics. Unique Features:- Offers a wealth of rich programming problems suitable for self-study - all with on-line judging at www programming-challenges.com- Presents practice training for all major programming contests - ACM International Collegiate Programming Contest (ACM ICPC), International Olympiad in Informatics (IOI), and Topcoder Challenge- Serves as a convenient, web-based means of adding a programming component to any algorithms or software engineering course - Contains complete working code for fundamental data structures and graph, string, numerical and geometric algorithms- Provides a brief-yet-thorough treatment of key elements in number theory, geometry, dynamic programming, and graph algorithms - Supports all popular programming languages (C, C++, Pascal, Java)Steven S.Skiena is a member of the faculty of computer science at SUNY Stony Brook and is author of many widely used books, including The Algorithm Design Manual. He received the 2001 IEEE Computer Society Undergraduate Teaching Award. Miguel Revilla is a member of the faculty of computer science at the University of Valladolid, Spain. He is official website archivist of the ACM ICPC and creator/maintainer of the primary robot-judge, contest-hosting website.