The complexity of today's statistical data calls for modern mathematical tools. Many fields of science make use of mathematical statistics and require continuous updating on statistical technologies. Practice makes perfect, since mastering the tools makes them applicable. Our book of exercises and solutions offers a wide range of applications and numerical solutions based on R. In modern mathematical statistics, the purpose is to provide statistics students with a number of basic exercises and also an understanding of how the theory can be applied to real-world problems. The application...
The complexity of today's statistical data calls for modern mathematical tools. Many fields of science make use of mathematical statistics and require...
This textbook provides a unified and self-contained presentation of the main approaches to and ideas of mathematical statistics. It collects the basic mathematical ideas and tools needed as a basis for more serious study or even independent research in statistics. The majority of existing textbooks in mathematical statistics follow the classical asymptotic framework. Yet, as modern statistics has changed rapidly in recent years, new methods and approaches have appeared. The emphasis is on finite sample behavior, large parameter dimensions, and model misspecifications. The present book...
This textbook provides a unified and self-contained presentation of the main approaches to and ideas of mathematical statistics. It collects the basic...
The 16 papers and extended abstracts represent a wide range of topics at the interface of algebra and computer science. No index. Annotation copyright Book News, Inc. Portland, Or.
The 16 papers and extended abstracts represent a wide range of topics at the interface of algebra and computer science. No index. Annotation copyright...
Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology. This book provides an introduction to the analysis of such data. The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and...
Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology. This...
This is the first book at the graduate textbook level to discuss analyzing financial data with S-PLUS. Its originality lies in the introduction of tools for the estimation and simulation of heavy tail distributions and copulas, the computation of measures of risk, and the principal component analysis of yield curves. The book is aimed at undergraduate students in financial engineering; master students in finance and MBA's, and to practitioners with financial data analysis concerns.
This is the first book at the graduate textbook level to discuss analyzing financial data with S-PLUS. Its originality lies in the introduction of ...
"I know it's trivial, but I have forgotten why." This is a slightly exaggerated characterization of the unfortunate attitude of many mathematicians toward the surrounding world. The point of departure of this book is the opposite.
This textbook on the theory of probability is aimed at graduate students, with the ideology that rather than being a purely mathematical discipline, probability theory is an intimate companion of statistics. The book starts with the basic tools, and goes on to chapters on inequalities, characteristic functions, convergence, followed by the three main...
"I know it's trivial, but I have forgotten why." This is a slightly exaggerated characterization of the unfortunate attitude of many mathematicians...
There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to ?nd all these topics covered in one place. The goal of this text is to provide readers with a single book where they can ?nd a brief account of many of the modern topics in nonparametric inference. The book is aimed at master's-level or Ph. D. -level statistics and computer science students. It is also suitable for researchersin statistics, machine lea- ing and data mining who want to get up to speed quickly on modern...
There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets m...
This book arose out of two graduate courses that the authors have taught duringthepastseveralyears;the?rstonebeingonmeasuretheoryfollowed by the second one on advanced probability theory. The traditional approach to a ?rst course in measure theory, such as in Royden (1988), is to teach the Lebesgue measure on the real line, then the p di?erentation theorems of Lebesgue, L -spaces on R, and do general m- sure at the end of the course with one main application to the construction of product measures. This approach does have the pedagogic advantage of seeing one concrete case ?rst before going...
This book arose out of two graduate courses that the authors have taught duringthepastseveralyears;the?rstonebeingonmeasuretheoryfollowed by the secon...
Though there are many recent additions to graduate-level introductory books on Bayesian analysis, none has quite our blend of theory, methods, and ap- plications. We believe a beginning graduate student taking a Bayesian course or just trying to find out what it means to be a Bayesian ought to have some familiarity with all three aspects. More specialization can come later. Each of us has taught a course like this at Indian Statistical Institute or Purdue. In fact, at least partly, the book grew out of those courses. We would also like to refer to the review (Ghosh and Samanta (2002b)) that...
Though there are many recent additions to graduate-level introductory books on Bayesian analysis, none has quite our blend of theory, methods, and ap-...
1 Audience Students seeking master's degrees in applied statistics in the late 1960s and 1970s typically took a year-long sequence in statistical methods. Popular choices of the course text book in that period prior to the availability of high speed computing and graphics capability were those authored by Snedecor and Cochran, and Steel and Torrie. By 1980, the topical coverage in these classics failed to include a great many new and important elementary techniques in the data analyst's toolkit. In order to teach the statistical methods sequence with adequate coverage of topics, it became...
1 Audience Students seeking master's degrees in applied statistics in the late 1960s and 1970s typically took a year-long sequence in statistical meth...
