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...
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 ...
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...
Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated...
Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood t...
Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. 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.
The book assumes an intermediate background in mathematics, computing, and...
Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. ...
Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import...
Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source ...
Every advance in computer architecture and software tempts statisticians to tackle numerically harder problems. To do so intelligently requires a good working knowledge of numerical analysis. This book equips students to craft their own software and to understand the advantages and disadvantages of different numerical methods. Issues of numerical stability, accurate approximation, computational complexity, and mathematical modeling share the limelight in a broad yet rigorous overview of those parts of numerical analysis most relevant to statisticians. In this second edition, the material on...
Every advance in computer architecture and software tempts statisticians to tackle numerically harder problems. To do so intelligently requires a good...
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...
This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic...
This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a va...
This book is concerned with the processing of signals that have been sam- pled and digitized. The fundamental theory behind Digital Signal Process- ing has been in existence for decades and has extensive applications to the fields of speech and data communications, biomedical engineering, acous- tics, sonar, radar, seismology, oil exploration, instrumentation and audio signal processing to name but a few 87]. The term "Digital Signal Processing," in its broadest sense, could apply to any operation carried out on a finite set of measurements for whatever purpose. A book on signal processing...
This book is concerned with the processing of signals that have been sam- pled and digitized. The fundamental theory behind Digital Signal Process- in...
