Statistical Computing

SAS Programming: The One-Day Course is a concise introduction to using the SAS programming language. It gives readers a start in SAS programming and the basic data manipulations and statistical summaries that are available through SAS. Unlike other introductory books on the market, this pocket-size..

This book provides a broad, accessible treatment of the theory and linguistic applications of semisupervised methods. It presents a brief history of the field before moving on to discuss well-known natural language processing methods, such as self-training and co-training. It then centers on machine..

Sharpening Your SAS Skills is an intermediate text on SAS programming and data management intended for those with some knowledge of the SAS language. It covers the most common tools used by SAS programmers and data analysts in their daily work. Designed as a quick-reference and practitioner's guide,..

Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research d..

Multilevel models and how to fit these models using R, how to employ multilevel modeling with longitudinal data, the valuable graphical options in R,  models for categorical dependent variables in both single level and multilevel data. Concludes with Bayesian fitting of multilevel models...

From basic data mining concepts to state-of-the-art advances, this book covers the theory of the subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification,..

The problem with programming is that you are always one typo away from writing something silly. Likewise with data analysis, a small mistake in your model can lead to a big mistake in your results. Combining the two disciplines means that it is all too easy for a missed minus sign to generate a fals..

Giving a broad perspective of the field from numerous vantage points, Text Mining focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filt..

This book provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, so..

This book provides documentation for a new version of the S system released in 1988. The new S enhances the features that have made S popular: interactive computing, flexible graphics, data management and a large collection of functions...

Identifying some of the most influential algorithms that are widely used in the data mining community, this volume provides a description of each algorithm, discusses the impact of the algorithms, and reviews current and future research on the algorithms. Thoroughly evaluated by independent reviewer..

Focusing on data mining mechanics and applications, this book explores the most common matrix decompositions, including singular value, semidiscrete, independent component analysis, non-negative matrix factorization, and tensors. It shows how these matrix decompositions can be used to analyze large ..