Bøger i Statistics and Computing serien

This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics.

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples.

An overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data.

This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R.

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. All ideas are illustrated with displays from analyses of real datasets.

The first part of the book deals with the heart of the R language and its fundamental concepts. This is followed by detailed explanations on how to perform many standard statistical analyses, mainly in Biostatistics.

This book describes an interactive statistical computing environment called 1 XploRe. XploRe is a matrix-oriented statistical language with a comprehensive set of basic statistical operations that provides highly interactive graphics, as well as a programming environ ment for user-written macros;

Lavishly illustrated with both detailed line drawings and clinical photos, this book offers comprehensive coverage of every aspect of the management of intestinal stomas.

In this book, Graham Wills bridges the gap between the art and the science of visually representing data. He does not simply give rules and advice, but bases these on general principles and provide a clear path between them.

Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books

The recent concept of universal (also called automatic or black-box) random variate generation can only be found dispersed in the literature. Examples of possible applications of the presented algorithms (including option pricing, VaR and Bayesian statistics) are presented at the end of the book.

This book offers a solid introduction to evolutionary computation for use in applied statistics research. It guides readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods.

Presenting aspects of numerical analysis applicable to statisticians, this volume enables students to craft their own software and to understand both the advantages and challenges of numerical methods. Topics include numerical stability, accurate approximations, computational complexity and more.

Stata and R are two very flexible data analysis packages. This book details how to extend the power of Stata through the use of R. It steps through more than thirty packages written in both languages, comparing and contrasting their different approaches.

Supplementary material, such as computer programs, are provided on the world wide web. Dr. Brusco is an editorial board member for the Journal of Classification, and a member of the Board of Directors for the Classification Society of North America.

This book is an integrated treatment of applied statistical methods, presented at an intermediate level. It serves as an advanced introduction to the SAS programming language as well as demonstrating how to use SAS to analyse of a wide variety of data.

This is the only advanced programming book on R, the enormously successful open-source system based on the S language. It guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages.

This book is concerned with the processing of signals that have been sam pled and digitized.

Computational inference uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation.

Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation.

Written for statisticians, computer scientists, geographers, researchers, and others interested in visualizing data. This book presents a foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems.

A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets.

The interface facilities are most developed onWindows (R) and these are covered with worked examples. The authors have written the widely used Modern Applied Statisticswith S-PLUS, now in its third edition, and several software librariesthat enhance S-PLUS and R;

It includes advances in methods for parallel random number generation, universal methods for generation of nonuniform variates, perfect sampling, and software for random number generation.

Accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Numerous exercises allow the text to be used for a first course in statistical computing or as supplementary text for various courses that emphasise computations.

This book systematically addresses the design and analysis of efficient techniques for independent random sampling.

This text explores developments and solutions for many practical problems confronting quantitative methods in financial research and industry. It is a synthesis of scientific contributions on practical implementation and theoretical concepts.

This book systematically addresses the design and analysis of efficient techniques for independent random sampling.

This text explores developments and solutions for many practical problems confronting quantitative methods in financial research and industry. It is a synthesis of scientific contributions on practical implementation and theoretical concepts.