Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods.
This book describes an interactive statistical computing environment called 1 XploRe. As the name suggests, support for exploratory statistical analysis is given by a variety of computational tools. 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; it offers hard-wired smoothing procedures for effective high-dimensional data analysis.
Its highly dynamic graphic capa bilities make it possible to construct student-level front ends for teaching basic elements of statistics. Hot keys make it an easy-to-use computing environment for statistical analysis. The primary objective of this book is to show how the XploRe system can be used as an effective computing environment for a large number of statistical tasks. The computing tasks we consider range from basic data matrix manipulations to interactive customizing of graphs and dynamic fit ting of high-dimensional statistical models.
The XploRe language is similar to other statistical languages and offers an interactive help system that can be extended to user-written algorithms. The language is intuitive and read ers with access to other systems can, without major difficulty, reproduce the examples presented here and use them as a basis for further investigation.
Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on the principles of numerical analysis and is intended to equip those readers who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods.
Author : Wendy L. Martinez,Angel R. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as. Cram Just the FACTS studyguides gives all of the outlines, highlights, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram is Textbook Specific. Accompanies: This item is printed on demand.
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology.
This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.
Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry.
The many topics include neural networks, support vector machines, classification trees and boostingthe first comprehensive treatment of this topic in any book. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics.
Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models.
Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book.
Jongbloed, Kwantitatieve Methoden, Issue 72B28, So, this … may be a good reference guide on the current state of statistics. The bibliography contains more than items and there are many WWW references in the text. Each chapter is accompanied by a good selection of challenging exercises …. Apart from its obvious use as a course text, this is a useful reference for any statistician who uses or wishes to use computationally intensive methods.
This is the third of a series …. I have enjoyed reading all of them. The bibliography alone makes it a valuable research tool …. Owen, Technometrics, Vol. Hand, Short Book Reviews, Vol. Skip to main content Skip to table of contents. Advertisement Hide. This service is more advanced with JavaScript available. Elements of Computational Statistics.
Authors view affiliations James E. Includes supplementary material: sn. Front Matter Pages i-xxi. The Basics of R. Pages Numerical Techniques. Combinatorics and Discrete Distributions. Univariate Distributions. Univariate Statistical Analysis. Multivariate Distributions. Regression Models. Multivariate Statistical Analysis.
Random Numbers in R. Advanced Graphical Techniques in R. Back Matter Pages
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