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Nonparametric statistics --- Mathematical statistics --- Statistique non-paramétrique --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Nonparametric statistics. --- 519.234 --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric --- 519.234 Non-parametric methods --- Non-parametric methods

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The large number of already available textbooks on stochastic calculus with specific applications to finance requires a justification for another contribution to this subject. The justifcation is mainly pedagogical. These lecture notes start with an elementary approach to stochastic calculus due to Föllmer, who showed that one can develop Ito's calculus "pathwise" as an exercise in real analysis. The text opens to students interested in finance a quick (but by no means "dirty") road to the tools required for advanced finance in continuous time, including option pricing by martingale methods, term structure models in a HJM-framework and the Libor market model. The reader is supposed only to be familiar with elementary real analysis (e.g. Taylor's Theorem) and basic probability theory. The text is also useful for mathematicians interested in the methods of modern mathematical finance without prior knowledge of advanced stochastic analysis.

Finance --- Stochastic analysis --- Mathematical models --- Finances --- Analyse stochastique --- Textbooks --- Modèles mathématiques --- Manuels --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Economics. --- Mathematics. --- Macroeconomics. --- Statistics. --- Distribution (Probability theory. --- Finance. --- Economics, general. --- Mathematics, general. --- Macroeconomics/Monetary Economics//Financial Economics. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Probability Theory and Stochastic Processes. --- Finance, general. --- Funding --- Funds --- Economics --- Currency question --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Math --- Science --- Economic theory --- Political economy --- Social sciences --- Economic man --- Textbooks. --- Management science. --- Statistics . --- Probabilities. --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Quantitative business analysis --- Management --- Problem solving --- Operations research --- Statistical decision

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Computational Genome Analysis: An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field. This book features:Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation. Presentation of fundamentals of probability, statistics, and algorithms. Implementation of computational methods with numerous examples based upon the R statistics package. Extensive descriptions and explanations to complement the analytical development. More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature. Exercises at the end of chapters. Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels. Simon Tavaré holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavaré's research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavaré is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics. Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.

Genetics --- Génétique --- Mathematical models --- Modèles mathématiques --- Mathematics. --- Genetics -- Mathematics. --- Genomics --- Sequence Analysis --- Genome --- Computational Biology --- Biology --- Genetic Structures --- Genetic Techniques --- Genetic Phenomena --- Biological Science Disciplines --- Investigative Techniques --- Phenomena and Processes --- Natural Science Disciplines --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Disciplines and Occupations --- Biology - General --- Health & Biological Sciences --- Mathematics --- Génétique --- Modèles mathématiques --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Computer science. --- Mathematical statistics. --- Bioinformatics. --- Biomathematics. --- Statistics. --- Computer Science. --- Computational Biology/Bioinformatics. --- Genetics and Population Dynamics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Probability and Statistics in Computer Science. --- Embryology --- Mendel's law --- Adaptation (Biology) --- Breeding --- Chromosomes --- Heredity --- Mutation (Biology) --- Variation (Biology) --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Bio-informatics --- Biological informatics --- Information science --- Computational biology --- Systems biology --- Informatics --- Science --- Data processing --- Statistics . --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics)

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"…the author has packaged an excellent and modern set of topics around the development and use of quantitative models.... If you need to learn about resampling, this book would be a good place to start." —Technometrics (Review of the Second Edition) This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. Only requiring minimal mathematics beyond algebra, the book provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware. Topics and Features * Practical presentation covers both the bootstrap and permutations along with the program code necessary to put them to work. * Includes a systematic guide to selecting the correct procedure for a particular application. * Detailed coverage of classification, estimation, experimental design, hypothesis testing, and modeling. * Suitable for both classroom use and individual self-study. New to the Third Edition * Procedures are grouped by application; a prefatory chapter guides readers to the appropriate reading matter. * Program listings and screen shots now accompany each resampling procedure: Whether one programs in C++, CART, Blossom, Box Sampler (an Excel add-in), EViews, MATLAB, R, Resampling Stats, SAS macros, S-PLUS, Stata, or StatXact, readers will find the program listings and screen shots needed to put each resampling procedure into practice. * To simplify programming, code for readers to download and apply is posted at http://www.springeronline.com/0-8176-4386-9. * Notation has been simplified and, where possible, eliminated. * A glossary and answers to selected exercises are included. With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods. It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences, engineering, and technology.

Statistical science --- Mathematical statistics --- Resampling (Statistics). --- Basic Sciences. Statistics --- Probability Theory, Sampling Theory. --- Mathematics. --- Mathematical analysis. --- Analysis (Mathematics). --- Probabilities. --- Statistics. --- Probability Theory and Stochastic Processes. --- Analysis. --- Statistical Theory and Methods. --- Statistics, general. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk --- 517.1 Mathematical analysis --- Mathematical analysis --- Math --- Science --- Resampling (Statistics) --- Resampling methods (Statistics) --- Nonparametric statistics --- Statistics --- Rééchantillonnage (Statistique) --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Distribution (Probability theory. --- Global analysis (Mathematics). --- Mathematical statistics. --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Analysis, Global (Mathematics) --- Differential topology --- Functions of complex variables --- Geometry, Algebraic --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistics .

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Time Series Analysis and Its Applications, Second Edition, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging, monitoring a nuclear test ban treaty, evaluating the volatility of an asset, or finding a gene in a DNA sequence. The book is designed to be useful as a text for graduate level students in the physical, biological and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Material from the first edition of the text has been updated by adding examples and associated code based on the freeware R statistical package. As in the first edition, modern developments involving categorical time series analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, GARCH models, stochastic volatility models, wavelets, and Monte Carlo Markov chain integration methods are incorporated in the text. In this edition, the material has been divided into smaller chapters, and the coverage of financial time series, including GARCH and stochastic volatility models, has been expanded. These topics add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models. R.H. Shumway is Professor of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He won the 1986 American Statistical Association Award for Outstanding Statistical Application and the 1992 Communicable Diseases Center Statistics Award; both awards were for joint papers on time series applications. He is the author of a previous 1988 Prentice-Hall text on applied time series analysis. D.S. Stoffer is Professor of Statistics at the University of Pittsburgh. He has made seminal contributions to the analysis of categorical time series and won the 1989 American Statistical Association Award for Outstanding Statistical Application in a joint paper analyzing categorical time series arising in infant sleep-state cycling. He is currently a Departmental Editor for the Journal of Forecasting and Associate Editor of the Annals of the Institute of Statistical Mathematics. .

Mathematical statistics --- Time-series analysis --- Série chronologique --- Série chronologique. --- Tijdreeksen. -- gtt. --- Time-series analysis. --- Toepassingen. -- gtt. --- Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- AA / International- internationaal --- 303.0 --- 304.0 --- 519.2 --- 304.5 --- 305.96 --- tijdreeksanalyse --- 519.246 --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Probabilities --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken). --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen. --- Wiskundige statistiek --- Techniek van de statistische-econometrische voorspellingen. Prognose in de econometrie. --- Macro-economisch model van een of verschillende landen. --- Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- Série chronologique. --- Tijdreeksen. --- Toepassingen. --- 519.246 Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- Série chronologique --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Statistics. --- Statistical Theory and Methods. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken) --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen --- Techniek van de statistische-econometrische voorspellingen. Prognose in de econometrie --- Macro-economisch model van een of verschillende landen --- Mathematical statistics. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Sampling (Statistics) --- Statistics . --- R (Computer program language). --- GNU-S (Computer program language) --- Domain-specific programming languages