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This book consists of four hundred exercises in mathematical statistics and their solutions, over 95% of which are in the author's Mathematical Statistics, Second Edition (Springer, 2003). For students preparing for work on a Ph.D. degree in statistics and instructors of mathematical statistics courses, this useful book provides solutions to train students for their research ability in mathematical statistics and presents many additional results and examples that complement any text in mathematical statistics. To develop problem-solving skills, two solutions and/or notes of brief discussions accompany a few exercises. The exercises are grouped into seven chapters with titles matching those in the author's Mathematical Statistics. On the other hand, the book is stand-alone because exercises and solutions are comprehensible independently of their source, and notation and terminology are explained in the front of the book. Readers are assumed to have a good knowledge in advanced calculus. A course in real analysis or measure theory is highly recommended. If this book is used with a statistics textbook that does not include probability theory, then knowledge in measure-theoretic probability theory is required. Jun Shao is Professor of Statistics at the University of Wisconsin, Madison.

Mathematical statistics --- Statistique mathématique --- Problems, exercises, etc. --- Problèmes et exercices --- Mathematical statistics. --- Mathematical statistics - Problems, exercises, etc. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Statistics --- Statistique mathématique --- Problèmes et exercices --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Statistics. --- Statistical Theory and Methods. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

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Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples, often from real-life and using real data, the authors can show how the fundamentals of probabilistic and statistical theories arise intuitively. It provides a tried and tested, self-contained course, that can also be used for self-study. A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to the students. In addition the book contains over 350 exercises, half of which have answers, of which half have full solutions. A website at www.springeronline.com/1-85233-896-2 gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite for the book is a first course in calculus; the text covers standard statistics and probability material, and develops beyond traditional parametric models to the Poisson process, and on to useful modern methods such as the bootstrap. This will be a key text for undergraduates in Computer Science, Physics, Mathematics, Chemistry, Biology and Business Studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects.

Mathematical statistics --- Probability theory --- Probabilities --- Probabilités --- Statistique mathématique --- Handbooks, manuals, etc. --- Guides, manuels, etc --- Harmonic analysis. --- Mathematical statistics -- Textbooks. --- Probabilities -- Textbooks. --- Probabilities. --- Stochastic analysis. --- Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Probabilités --- Statistique mathématique --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Probability --- Statistical inference --- Mathematics. --- Statistics. --- Applied mathematics. --- Engineering mathematics. --- Probability Theory and Stochastic Processes. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Appl.Mathematics/Computational Methods of Engineering. --- Combinations --- Chance --- Least squares --- Risk --- Distribution (Probability theory. --- Mathematical and Computational Engineering. --- Engineering --- Engineering analysis --- Mathematical analysis --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistics . --- Probabilités. --- Statistique mathématique.

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Copulas are functions that join multivariate distribution functions to their one-dimensional margins. The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions. With 116 examples, 54 figures, and 167 exercises, this book is suitable as a text or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics, although some familiarity with nonparametric statistics would be useful. Knowledge of measure-theoretic probability is not required. The revised second edition includes new sections on extreme value copulas, tail dependence, and quasi-copulas. Roger B. Nelsen is Professor of Mathematics at Lewis & Clark College in Portland, Oregon. He is also the author of Proofs Without Words: Exercises in Visual Thinking and Proofs Without Words II: More Exercises in Visual Thinking, published by the Mathematical Association of America.

Stochastic processes --- Mathematical statistics --- Copulas (Mathematical statistics) --- Distribution (Probability theory) --- Copules (Statistique mathématique) --- Distribution (Théorie des probabilités) --- Copulas (Mathematical statistics). --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- 519.5 --- Copulas (mathematical statistics) --- Copules (Statistique mathématique) --- Distribution (Théorie des probabilités) --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Mathematics. --- Computer simulation. --- Economics, Mathematical. --- Probabilities. --- Statistics. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Quantitative Finance. --- Simulation and Modeling. --- Distribution (Probability theory. --- Mathematical statistics. --- Finance. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Funding --- Funds --- Economics --- Currency question --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Sampling (Statistics) --- Statistics . --- Economics, Mathematical . --- Mathematical economics --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Methodology

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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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Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace to-day? This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases or large in numbers of variables or large in both. Data visualization is useful for data cleaning, exploring data, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining. Data analysts, statisticians, computer scientists-indeed anyone who has to explore a large dataset of their own-should benefit from reading this book. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. There are considerable advantages in extending displays which are well-known and well-tried, both in understanding how best to make use of them in your work and in presenting results to others. It should also make the book readily accessible for readers who already have a little experience of drawing statistical graphics. All ideas are illustrated with displays from analyses of real datasets and the authors emphasize the importance of interpreting displays effectively. Graphics should be drawn to convey information and the book includes many insightful examples. Antony Unwin holds the Chair of Computer Oriented Statistics and Data Analysis at the University of Augsburg. He has been involved in developing visualization software for twenty years. Martin Theus is a Senior Researcher at the University of Augsburg, has worked in industry and research in both Germany and the USA, and is the author of the visualization software Mondrian. Heike Hofmann is Assistant Professor of Statistics at Iowa State University. She wrote the software MANET and has also cooperated in the development of the GGobi software.

Artificial intelligence. Robotics. Simulation. Graphics --- Information systems --- QA 276.3 Graphic methods --- Computational Science & Engineering --- Computational Statistics --- Computer Imaging, Graphics & Vision --- Computer Science --- Statistics --- Computer graphics. --- Statistique --- Infographie --- Graphic methods. --- Méthodes graphiques --- Computer graphics --- Graphic methods --- Statistics -- Graphic methods. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Méthodes graphiques --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Automatic drafting --- Graphic data processing --- Graphics, Computer --- Diagrams, Statistical --- Statistical diagrams --- Mathematics. --- Operations research. --- Decision making. --- Data mining. --- Visualization. --- Computer software. --- Statistics. --- Mathematical Software. --- Statistical Theory and Methods. --- Data Mining and Knowledge Discovery. --- Operation Research/Decision Theory. --- Computer Graphics. --- Computer art --- Graphic arts --- Electronic data processing --- Engineering graphics --- Image processing --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Software, Computer --- Computer systems --- Visualisation --- Imagery (Psychology) --- Imagination --- Visual perception --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Math --- Science --- Digital techniques --- Decision making --- Curve fitting --- Mathematical statistics. --- Operations Research/Decision Theory. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistics . --- Statistics - Graphic methods