Katalog książek anglojęzycznych

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1 Katalog książek anglojęzycznych statystyka matematyczna Amerykańskie Towarzystwo Statystyczne ogłosiło rok 2013 Międzynarodowym Rokiem Statystyki. Ogólnoświatowa akcja ma na celu uczczenie i docenienie osiągnięć nauk statystycznych. Przyłączyliśmy się do akcji promując najważniejsze książki z tej dziedziny. Przeglądaj katalog z nowościami i najciekawszymi publikacjami. Dowiedz się więcej na

2 Statystyka matematyczna A Brief Course in Mathematical Statistics Elliot A. Tanis Pearson Education An Introduction to Mathematical Statistics and Its Applications Richard J. Larsen Pearson Education ,99 For a one-semester course in Mathematical Statistics. This innovative new introduction to Mathematical Statistics covers the important concept of estimation at a point much earlier than other texts (Chapter 2). Thought-provoking pedagogical aids help students test their understanding and relate concepts to everyday life. Ideal for courses that offer a little less probability than usual, this book requires one year of calculus as a prerequisite Oprawa: miękka 66,99 Noted for its integration of real-world data and case studies, this text offers sound coverage of the theoretical aspects of mathematical statistics. The authors demonstrate how and when to use statistical methods, while reinforcing the calculus that students have mastered in previous courses. Throughout thefifth Edition, the authors have added and updated examples and case studies, while also refining existing features that show a clear path from theory to practice. Antieigenvalue Analysis Karl E. Gustafson World Scientific Publishing Applied Functional Analysis J. Tinsley Oden ,00 Karl Gustafson is the creator of the theory of antieigenvalue analysis. Its applications spread through fields as diverse as numerical analysis, wavelets, statistics, quantum mechanics, and finance. Antieigenvalue analysis, with its operator trigonometry, is a unifying language which enables new and deeper geometrical understanding of essentially every result in operator theory and matrix theory, together with their applications. This book will open up its methods to a wide range of specialists ,00 Through numerous illustrative examples and comments, Applied Functional Analysis, Second Edition demonstrates the rigor of logic and systematic, mathematical thinking. It presents the mathematical foundations that lead to classical results in functional analysis. More specifically, the text prepares students to learn the variational theory of partial differential equations, distributions and Sobolev spaces, and numerical analysis with an emphasis on finite element methods. While retaining the structure of its best-selling predecessor, this second edition includes revisions of many original examples, along with new examples that often reflect the authors' own vast research experiences and perspectives. This edition also provides many more exercises as well as a solutions manual for qualifying instructors. Each chapter begins with an extensive introduction and concludes with a summary and historical comments that frequently refer to other sources. Basic Practice of Statistics David S. Moore Palgrave MacMillan Foundations of Statistical Algorithms Claus Weihs ,99 The Basic Practice of Statistics (BPS) is designed to give a working knowledge of the ideas and tools of practical statistics. Through accessible language and engaging examples and exercises based on real data, BPS explains both the mechanics and the concepts needed for statistical work. The Sixth Edition maintains the textbook's focus on statistical reasoning and real-life situations and data, while updating the organisation, exercises and examples to better reflect the needs and experiences of today's student. Here are a few highlights of the revised Sixth Edition: Updated Exercises and Examples: More than 20 percent of all examples and exercises have been revised or updated. Contemporary situations and up-todate data cover a number of fields, including environmental studies, agriculture, business and finance, technology, biology, psychology, health and medicine, education, sports, and entertainment. New examples and exercises draw on topics such as texting while driving, CO2 emissions, the Gulf oil spill, and how weather influences tipping ,99 Reviewing the historical development of basic algorithms to illuminate the evolution of today s more powerful statistical algorithms, this comprehensive textbook emphasizes recurring themes in all statistical algorithms including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, it touches on topics not usually covered in similar books, namely, systematic verification and the scaling of many established techniques to very large databases. Broadly accessible, it offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website. 2

3 Statystyka matematyczna Introduction to Mathematical Statistics Robert V. Hogg Pearson Education Introduction to Partial Differential Equations for Scientists and Engineers Using Mathematica Kuzman Adzievski Oprawa: miękka 55,99 Introduction to Mathematical Statistics, Seventh Edition, offers a proven approach designed to provide you with an excellent foundation in mathematical statistics. Ample examples and exercises throughout the text illustrate concepts to help you gain a solid understanding of the material ,99 With a special emphasis on engineering and science applications, this textbook provides a mathematical introduction to PDEs at the undergraduate level. It takes a new approach to PDEs by presenting computation as an integral part of the study of differential equations. The authors use Mathematica(R) along with graphics to improve understanding and interpretation of concepts. They also present exercises in each chapter and solutions to selected examples. Topics discussed include Laplace and Fourier transforms as well as Sturm-Liouville boundary value problems. Linear Algebra and Matrix Computations for Statistics Sudipto Banerjee Mathematical Statistics with Resampling and R Laura M. Chihara ,99 Linear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector-space approach, this book provides an understanding of the major concepts that underlie linear algebra and matrix analysis. Each chapter introduces a key topic, such as infinite-dimensional spaces, and provides illustrative examples. The authors examine recent developments in diverse fields such as spatial statistics, machine learning, data mining, and social network analysis. Complete in its coverage and accessible to students without prior knowledge of linear algebra, the text also includes results that are useful for traditional statistical applications ,95 Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis Calculation of sampling distributions The Central Limit Theorem Monte Carlo sampling Methods of Multivariate Analysis Alvin C. Rencher Modern Mathematical Statistics with Applications Jay L. Devore Springer ,95 Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. This Third Edition continues to explore the key descriptive and inferential procedures that result from multivariate analysis. Following a brief overview of the topic, the book goes on to review the fundamentals of matrix algebra, sampling from multivariate populations, and the extension of common univariate statistical procedures (including t-tests, analysis of variance, and multiple regression) to analogous multivariate techniques that involve several dependent variables. The latter half of the book describes statistical tools that are uniquely multivariate in nature, including procedures for discriminating among groups, characterizing lowdimensional latent structure in high-dimensional data, identifying clusters in data, and graphically illustrating relationships in low-dimensional space ,95 Many mathematical statistics texts are heavily oriented toward a rigorous mathematical development of probability and statistics, without much attention paid to how statistics is actually used. In contrast, Modern Mathematical Statistics with Applications, Second Edition strikes a balance between mathematical foundations and statistical practice. In keeping with the recommendation that every math student should study statistics and probability with an emphasis on data analysis, accomplished authors Jay Devore and Kenneth Berk make statistical concepts and methods clear and relevant through careful explanations and a broad range of applications involving real data. The main focus of the book is on presenting and illustrating methods of inferential statistics that are useful in research. It begins with a chapter on descriptive statistics that immediately exposes the reader to real data. The next six chapters develop the probability material that bridges the gap between descriptive and inferential statistics. Point estimation, inferences based on statistical intervals, and hypothesis testing are then introduced in the next three chapters. 3

4 Statystyka matematyczna Nonparametric Tests Vilijandas Bagdonavieus Probability, Statistics, and Stochastic Processes Peter Olofsson ,95 This book concerns testing hypotheses in non-parametric models. Classical nonparametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed ,5 Thoroughly updated to showcase the interrelationships between probability, statistics, and stochastic processes, Probability, Statistics, and Stochastic Processes, Second Edition prepares readers to collect, analyze, and characterize data in their chosen fields. Beginning with three chapters that develop probability theory and introduce the axioms of probability, random variables, and joint distributions, the book goes on to present limit theorems and simulation. The authors combine a rigorous, calculusbased development of theory with an intuitive approach that appeals to readers' sense of reason and logic. Including more than 400 examples that help illustrate concepts and theory, the Second Edition features new material on statistical inference and a wealth of newly added topics, including: Consistency of point estimators Large sample theory Bootstrap simulation Sequential Analysis: Hypothesis Testing and Change-Point Detection Michele Basseville Some Recent Advances in Mathematics and Statistics Yogendra P. Chaubey World Scientific Publishing ,99 This book presents an overview of the theory and applications of sequential methods for hypothesis testing and changepoint detection in a wide range of engineering and environmental domains. It describes all the important theoretical developments with an emphasis on applications, including environmental surveillance, biomedical engineering, image processing, computer security, econometrics, and finance. The text covers more practical discrete-time models as well as very general cases that include both continuous- and discrete-time models. In addition, it presents the results for multi-hypothesis tests and detection-isolation procedures ,00 This volume consists of a series of research papers presented at the conference Statistics 2011 Canada: 5th Canadian Conference in Applied Statistics held together with the 20th conference of the Forum for Interdisciplinary Mathematics titled, "Interdisciplinary Mathematical & Statistical Techniques". These papers cover a wide range of topics from applications of Mathematics and Statistics such as Selection Bias in Surveys, Biomarker Discovery, Analysis of Earth Temperature, Supply Chain Management, Trimmed ANOVA, Zero-inflated Data, Non-Gaussian Time Series, and Stochastic Ordering; Classification, Nonparametric Test, and Jackknifed Ridge Estimator; Bayes Factor; Random Graphs and Error Correcting Codes; Meta Analysis; and National Health Plans and Risk Reduction through Supply Chain. The topics have been reviewed by experts in the field and the selected papers are expected to provide a topical resource on the subjects concerned. Statistical Inference: A Short Course Michael J. Panik Statistical Methods for Stochastic Differential Equations Mathieu Kessler ,95 tatistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causality. To ensure a thorough understanding of all key concepts, Statistical Inference provides numerous examples and solutions along with complete and precise answers to many fundamental questions, including: How do we determine that a given dataset is actually a random sample? With what level of precision and reliability can a population sample be estimated? How are probabilities determined and are they the same thing as odds? How can we predict the level of one variable from that of another? ,99 The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations. Written to be accessible to both new students and seasoned researchers, each self-contained chapter starts with introductions to the topic at hand and builds gradually towards discussing recent research. The book covers Wiener-driven equations as well as stochastic differential equations with jumps, including continuous-time ARMA processes and COGARCH processes. It presents a spectrum of estimation methods, including nonparametric estimation as well as parametric estimation based on likelihood methods, estimating functions, and simulation techniques. Two chapters are devoted to high-frequency data. Multivariate models are also considered, including partially observed systems, asynchronous sampling, tests for simultaneous jumps, and multiscale diffusions. 4

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