Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers that appear to be random. These "pseudorandom" numbers must pass statistical tests just as random samples would. Methods for producing pseudorandom numbers and transforming those numbers to simulate samples from various distributions are among the most important topics in statistical computing. This book surveys techniques of random number generation and the use of random numbers in Monte Carlo simulation. The book covers basic principles, as...
Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers ...
Many books teach computational statistics. Until now, however, none has shown how to write a good program. This book gives statisticians, biostatisticians and methodologically-oriented researchers the tools they need to develop high-quality statistical software.
Topics include how to:
Program in Fortran 95 using a pseudo object-oriented style
Write accurate and efficient computational procedures
Create console applications
Build dynamic-link libraries (DLLs) and Windows-based software components
Develop graphical user interfaces (GUIs)
...
Many books teach computational statistics. Until now, however, none has shown how to write a good program. This book gives statisticians, biostatis...
Preface to First Edition Before writing the graphics for SYSTAT in the 1980 s, I began by teaching a seminar in statistical graphics and collecting as many different quantitative graphics as I could find. I was determined to produce a package that could draw every statistical graphic I had ever seen. The structure of the program was a collection of procedures named after the basic graph types they p- duced. The graphics code was roughly one and a half megabytes in size. In the early 1990 s, I redesigned the SYSTAT graphics package using - ject-based technology. I intended to produce a more...
Preface to First Edition Before writing the graphics for SYSTAT in the 1980 s, I began by teaching a seminar in statistical graphics and collecting as...
Thisisnowthefourtheditionof"TheBasicsofS-Plus"since1997.S-Plus saw a steady growth in popularity, and it established itself in many edu- tional and business places as a major data analysis tool.S-Plus is valued for its modern, interactive data analysis environment, whether it is the p- mary system or a complement to other standards like SAS (the latter is in particular true for the industry we work in, pharmaceuticals). We have followed the various releases with new editions of our book, introducing over time major changes like the incorporation of S Version 4 (the underlying language),...
Thisisnowthefourtheditionof"TheBasicsofS-Plus"since1997.S-Plus saw a steady growth in popularity, and it established itself in many edu- tional and bu...
Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace to-day? 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. Data visualization is useful for data cleaning, exploring data, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining. Data analysts, statisticians, computer scientists-indeed anyone who has to...
Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace to-day? This book shows how ...
In recent years developments in statistics have to a great extent gone hand in hand with developments in computing. Indeed, many of the recent advances in statistics have been dependent on advances in computer science and techn- ogy. Many of the currently interesting statistical methods are computationally intensive, eitherbecausetheyrequireverylargenumbersofnumericalcompu- tions or because they depend on visualization of many projections of the data. The class of statistical methods characterized by computational intensity and the supporting theory for such methods constitute a discipline...
In recent years developments in statistics have to a great extent gone hand in hand with developments in computing. Indeed, many of the recent advance...
Numerical linear algebra is one of the most important subjects in the field of statistical computing. Statistical methods in many areas of application require computations with vectors and matrices. This book describes accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Although the book is not tied to any particular software system, it describes and gives examples of the use of modern computer software for numerical linear algebra. An understanding of numerical linear algebra requires basic...
Numerical linear algebra is one of the most important subjects in the field of statistical computing. Statistical methods in many areas of application...
Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, and extensions to local likelihood and density estimation. Basic theoretical results and diagnostic tools such as cross validation are introduced along the way. Examples illustrate the implementation of the methods using the LOCFIT software.
Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometr...
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. The authors present a unified model-building strategy for both models and apply this to the analysis of over 20 real datasets from a wide variety of areas, including pharmacokinetics, agriculture, and manufacturing. Much emphasis is placed on the use of graphical displays at the various phases of the model-building process, starting with exploratory plots of the data and concluding with diagnostic plots to...
An overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, rep...
S is a high-level language for manipulating, analysing and displaying data. It forms the basis of two highly acclaimed and widely used data analysis software systems, the commercial S-PLUS(R) and the Open Source R. This book provides an in-depth guide to writing software in the S language under either or both of those systems. It is intended for readers who have some acquaintance with S language and want to know how to use it more effectively, for example to build re-usable tools for streamlining routine data analysis or to implement new statistical methods. One ofhe most outstanding...
S is a high-level language for manipulating, analysing and displaying data. It forms the basis of two highly acclaimed and widely used data analysis s...
